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Peter Rousseeuw

Not to be confused with: Peter Rousseau

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Wikipedia or ReplicationWiki mentions

(Only mentions on Wikipedia that link back to a page on a RePEc service)
  1. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April.

    Mentioned in:

    1. Econometric applications of high-breakdown robust regression techniques (EL 2001) in ReplicationWiki ()

Working papers

  1. Luc Aucremanne & Guy Brys & Mia Hubert & Peter J. Rousseeuw & Anja Struyf, 2002. "Inflation, relative prices and nominal rigidities," Working Paper Research 20, National Bank of Belgium.

    Cited by:

    1. María Ángeles Caraballo & Carlos Usabiaga, 2006. "Análisis Desagregado de la Inflación: Una Aplicación Regional," Economic Working Papers at Centro de Estudios Andaluces E2006/07, Centro de Estudios Andaluces.
    2. Kausik Chaudhuri & Matthew Greenwood-Nimmo & Minjoo Kim & Yongcheol Shin, 2013. "On the Asymmetric U-Shaped Relationship between Inflation, Inflation Uncertainty, and Relative Price Skewness in the UK," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 45(7), pages 1431-1449, October.
    3. Aucremanne, Luc & Druant, Martine, 2005. "Price-setting behaviour in Belgium: what can be learned from an ad hoc survey?," Working Paper Series 448, European Central Bank.
    4. Sartaj Rasool Rather & S. Raja Sethu Durai & M. Ramachandran, 2015. "Price Rigidity, Inflation and the Distribution of Relative Price Changes," South Asian Journal of Macroeconomics and Public Finance, , vol. 4(2), pages 258-287, December.
    5. Patrick Bisciari & Alain Durré & Alain Nyssens, 2003. "Stock market valuation in the United States," Working Paper Document 41, National Bank of Belgium.
    6. M. Angeles Caraballo & Carlos Dabus & Carlos Usabiaga, 2006. "Relative prices and inflation: new evidence from different inflationary contexts," Applied Economics, Taylor & Francis Journals, vol. 38(16), pages 1931-1944.
    7. A. Nazif Çatik & Christopher Martin & A. Özlem Onder, 2011. "Relative price variability and the Phillips Curve: evidence from Turkey," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 38(5), pages 546-561, September.
    8. María Ángeles Caraballo & Carlos Usabiaga, 2003. "Análisis de la estructura de la inflación de las regiones españolas: La metodología de Ball y Mankiw," Economic Working Papers at Centro de Estudios Andaluces E2003/44, Centro de Estudios Andaluces.
    9. Constantina Kottaridi & Diego Mendez-Carbajo & Dimitrios Thomakos, 2007. "Inflation Dynamics and the Cross-Sectional Distribution of Prices in the E.U. Periphery," Working Papers 0004, University of Peloponnese, Department of Economics.
    10. María Ángeles Caraballo & Carlos Usabiaga, 2006. "The Relevance of Supply Shocks for Inflation: The Spanish Case," Economic Working Papers at Centro de Estudios Andaluces E2006/17, Centro de Estudios Andaluces.
    11. Craigwell, Roland & Moore, Winston & Morris, Diego & Worrell, DeLisle, 2011. "Price Rigidity: A Survey of Evidence From Micro-Level Data," MPRA Paper 40927, University Library of Munich, Germany.
    12. Silvia Fabiani & Angela Gattulli & Roberto Sabbatini, 2004. "The pricing behaviour of Italian firms: new survey evidence on price stickiness," Temi di discussione (Economic working papers) 515, Bank of Italy, Economic Research and International Relations Area.
    13. Aucremanne, Luc & Dhyne, Emmanuel, 2004. "How frequently do prices change? Evidence based on the micro data underlying the Belgian CPI," Working Paper Series 331, European Central Bank.
    14. Fabio Rumler & Alfred Stiglbauer & Josef Baumgartner, 2011. "Patterns and Determinants of Price Changes: Analysing Individual Consumer Prices in Austria," German Economic Review, Verein für Socialpolitik, vol. 12(3), pages 336-350, August.
    15. José Contreras & Nora Guarata, 2013. "Inflation and relative price variability in Venezuela," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 38(36), pages 85-122, july-dece.
    16. Sartaj Rasool Rather, 2016. "Asymmetric Impact of Relative Price Shocks in Presence of Trend Inflation," Working Papers 2016-153, Madras School of Economics,Chennai,India.
    17. Maria A. Caraballo & Carlos Usabiaga, 2006. "Inflation and Supply Shocks in Spain: A Regional Approach," ERSA conference papers ersa06p335, European Regional Science Association.
    18. Silvia Fabiani & Angela Gattulli & Roberto Sabbatini, 2003. "La rigidità dei prezzi in Italia," Moneta e Credito, Economia civile, vol. 56(223), pages 325-358.
    19. Caraballo Pou, M. Angeles & Dabus, Carlos, 2008. "Nominal rigidities, skewness and inflation regimes," Research in Economics, Elsevier, vol. 62(1), pages 16-33, March.
    20. Sartaj Rather, 2016. "Asymmetric Impact of Relative Price Shocks in Presence of Trend Inflation," Working Papers id:11477, eSocialSciences.
    21. Geert Langenus, 2006. "Fiscal sustainability indicators and policy design in the face of ageing," Working Paper Research 102, National Bank of Belgium.
    22. Claudio E. V. Borio & Wiliam English & Andrew Filardo, 2003. "A tale of two perspectives: old or new challenges for monetary policy?," BIS Working Papers 127, Bank for International Settlements.
    23. Luc Aucremanne & Martine Druant, 2005. "Price-setting behaviour in Belgium: what can be learned from an ad hoc survey ?," Working Paper Research 65, National Bank of Belgium.

  2. Elvira Haezendonck & Greet Pison & Peter Rousseeuw & Anja Struyf & Alain Verbeke, 2000. "The Competitive Advantage of Seaports," ULB Institutional Repository 2013/328281, ULB -- Universite Libre de Bruxelles.

    Cited by:

    1. J. Augusto Felício & Manuela Batista & Michael Dooms & Vítor Caldeirinha, 2023. "How do sustainable port practices influence local communities’ perceptions of ports?," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(2), pages 351-380, June.
    2. Junghyun Yoon & Jaehoon Rhee & Alisher Tohirovich Dedahanov, 2017. "The roles of networks among innovators in regional innovation: comparative analysis between China and South Korea," European Planning Studies, Taylor & Francis Journals, vol. 25(5), pages 790-804, May.
    3. Peng, Peng & Yang, Yu & Lu, Feng & Cheng, Shifen & Mou, Naixia & Yang, Ren, 2018. "Modelling the competitiveness of the ports along the Maritime Silk Road with big data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 852-867.
    4. Izabela Kotowska & Marta Mańkowska & Michał Pluciński, 2018. "Inland Shipping to Serve the Hinterland: The Challenge for Seaport Authorities," Sustainability, MDPI, vol. 10(10), pages 1-17, September.
    5. Kammoun, Rabeb & Abdennadher, Chokri, 2022. "Seaport efficiency and competitiveness in European seaports," Transport Policy, Elsevier, vol. 121(C), pages 113-124.
    6. Elvira Haezendonck & Julien van den Broeck & Tim Jans, 2011. "Analysing the lobby-effect of port competitiveness’ determinants: a stochastic frontier approach," Journal of Productivity Analysis, Springer, vol. 36(2), pages 113-123, October.
    7. Zhang, Wenjun & Deng, Weibing & Li, Wei, 2018. "Statistical properties of links of network: A survey on the shipping lines of Worldwide Marine Transport Network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 218-227.
    8. Shi, Xin & Jiang, Haizhou & Li, Huan & Xu, Dong, 2020. "Maritime cluster research: Evolutionary classification and future development," Transportation Research Part A: Policy and Practice, Elsevier, vol. 133(C), pages 237-254.

  3. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2000. "Econometric applications of high-breakdown robust regression techniques," MPRA Paper 41529, University Library of Munich, Germany.

    Cited by:

    1. Cizek, P., 2004. "General Trimmed Estimation : Robust Approach to Nonlinear and Limited Dependent Variable Models," Discussion Paper 2004-130, Tilburg University, Center for Economic Research.
    2. Cizek, P., 2007. "General Trimmed Estimation : Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)," Discussion Paper 2007-65, Tilburg University, Center for Economic Research.
    3. Akarca, Ali T. & Tansel, Aysit, 2012. "Southwest as the New Internal Migration Destination in Turkey," IZA Discussion Papers 6627, Institute of Labor Economics (IZA).
    4. Giorgio Fagiolo & Mauro Napoletano & Andrea Roventini, 2008. "Are output growth-rate distributions fat-tailed? some evidence from OECD countries," Post-Print hal-03417062, HAL.
    5. Sunil Sapra, 2003. "High-breakdown point estimation of some regression models," Applied Economics Letters, Taylor & Francis Journals, vol. 10(14), pages 875-878.
    6. Elyasiani, Elyas & Movaghari, Hadi, 2022. "Determinants of corporate cash holdings: An application of a robust variable selection technique," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 967-993.
    7. Cizek, P., 2010. "Reweighted Least Trimmed Squares : An Alternative to One-Step Estimators," Other publications TiSEM 850c8dcb-835b-4d68-ab98-6, Tilburg University, School of Economics and Management.
    8. Cheng, Tsung-Chi, 2011. "Robust diagnostics for the heteroscedastic regression model," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1845-1866, April.
    9. Ali Akarca & Aysit Tansel, 2015. "Impact of internal migration on political participation in Turkey," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-14, December.
    10. Picchio, Matteo & Ubaldi, Michele, 2022. "Unemployment and Health: A Meta-Analysis," IZA Discussion Papers 15433, Institute of Labor Economics (IZA).
    11. Arzdar Kiraci, 2013. "Confirmation, Correction and Improvement for Outlier Validation Using Dummy Variables: t-Statistics or F-Incremental Statistics is not enough in OLS," International Econometric Review (IER), Econometric Research Association, vol. 5(2), pages 43-52, September.
    12. Berggren, Niclas & Elinder, Mikael, 2010. "Is Tolerance Good or Bad for Growth?," Working Paper Series 846, Research Institute of Industrial Economics.
    13. Akarca, Ali T. & Tansel, Aysit, 2015. "Voter Reaction to Government Incompetence and Corruption Related to the 1999 Earthquakes in Turkey," IZA Discussion Papers 9162, Institute of Labor Economics (IZA).
    14. William Ginn, 2022. "Climate Disasters and the Macroeconomy: Does State-Dependence Matter? Evidence for the US," Economics of Disasters and Climate Change, Springer, vol. 6(1), pages 141-161, March.
    15. Ali T. Akarca & Aysit Tansel, 2012. "Turkish Voter Response to Government Incompetence and Corruption Related to the 1999 Earthquakes," ERC Working Papers 1202, ERC - Economic Research Center, Middle East Technical University, revised Jan 2012.
    16. Doppelhofer, G. & Weeks, M., 2011. "Robust Growth Determinants," Cambridge Working Papers in Economics 1117, Faculty of Economics, University of Cambridge.
    17. Niclas Berggren & Mikael Elinder & Henrik Jordahl, 2008. "Trust and growth: a shaky relationship," Empirical Economics, Springer, vol. 35(2), pages 251-274, September.
    18. Čίžek, Pavel & Härdle, Wolfgang Karl, 2006. "Robust econometrics," SFB 649 Discussion Papers 2006-050, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    19. Michele Aquaro & Pavel Čížek, 2014. "Robust estimation of dynamic fixed-effects panel data models," Statistical Papers, Springer, vol. 55(1), pages 169-186, February.
    20. Burhan, Nik Ahmad Sufian & Che Razak, Razli & Rosli, Muhamad Ridhwan & Selamat, Muhamad Rosli, 2017. "The Bell Curve of Intelligence, Economic Growth and Technological Achievement: How Robust is the Cross-Country Evidence?," MPRA Paper 77469, University Library of Munich, Germany.
    21. Čížek, Pavel, 2002. "Robust estimation with discrete explanatory variables," SFB 373 Discussion Papers 2002,76, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    22. Wang, Zhipeng & Zhang, Mei & Ageli, Mohammed Moosa, 2022. "Revisiting resource curse hypothesis and sustainable development: Evaluating the role of financial risk for USA," Resources Policy, Elsevier, vol. 79(C).
    23. Carsten Colombier, 2011. "Does the composition of public expenditure affect economic growth? Evidence from the Swiss case," Applied Economics Letters, Taylor & Francis Journals, vol. 18(16), pages 1583-1589.
    24. Baldauf, Markus & Santos Silva, J.M.C., 2012. "On the use of robust regression in econometrics," Economics Letters, Elsevier, vol. 114(1), pages 124-127.
    25. Garciga, Christian & Verbrugge, Randal, 2021. "Robust covariance matrix estimation and identification of unusual data points: New tools," Research in Economics, Elsevier, vol. 75(2), pages 176-202.
    26. Cizek, P., 2004. "Asymptotics of Least Trimmed Squares Regression," Other publications TiSEM dab5d551-aca6-40bf-b92e-c, Tilburg University, School of Economics and Management.
    27. Anthony Atkinson, 2009. "Econometric Applications of the Forward Search in Regression: Robustness, Diagnostics, and Graphics," Econometric Reviews, Taylor & Francis Journals, vol. 28(1-3), pages 21-39.
    28. Colombier, Carsten, 2004. "Government and growth," MPRA Paper 104938, University Library of Munich, Germany.
    29. Elvira Sojli & Wing Wah Tham, 2017. "Foreign political connections," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 48(2), pages 244-266, February.
    30. Goeyvaerts, Geert & Buyst, Erik, 2019. "Do market rents reflect user costs?," Journal of Housing Economics, Elsevier, vol. 44(C), pages 112-130.
    31. Aquaro, M., 2013. "Pairwise difference estimation of linear panel data," Other publications TiSEM 2786f9bb-fbe1-4bac-8efc-b, Tilburg University, School of Economics and Management.
    32. Cheng, Tsung-Chi, 2005. "Robust regression diagnostics with data transformations," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 875-891, June.
    33. Toshiaki Tsukurimichi & Yu Inatsu & Vo Nguyen Le Duy & Ichiro Takeuchi, 2022. "Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 74(6), pages 1197-1228, December.
    34. Cizek, P. & Aquaro, M., 2015. "Robust Estimation and Moment Selection in Dynamic Fixed-effects Panel Data Models," Other publications TiSEM 39d0f613-007f-4d21-b1e2-b, Tilburg University, School of Economics and Management.
    35. Catherine Dehon & Marjorie Gassner & Vincenzo Verardi, 2005. "Robustness or Efficiency, A Test to Solve the Dilemma," Econometrics 0508011, University Library of Munich, Germany.
    36. Catherine Dehon & Marjorie Gassner & Vincenzo Verardi, 2009. "Beware of ‘Good’ Outliers and Overoptimistic Conclusions," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 437-452, June.
    37. Matthew, Salois, 2010. "Obesity and Diabetes, the Built Environment, and the ‘Local’ Food Economy," MPRA Paper 27945, University Library of Munich, Germany.
    38. Cheng, Tsung-Chi & Biswas, Atanu, 2008. "Maximum trimmed likelihood estimator for multivariate mixed continuous and categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2042-2065, January.
    39. Yasuhiro Yamakawa & Mike W. Peng & David L. Deeds, 2015. "Rising from the Ashes: Cognitive Determinants of Venture Growth after Entrepreneurial Failure," Entrepreneurship Theory and Practice, , vol. 39(2), pages 209-236, March.
    40. Peter Winker & Marianna Lyra & Chris Sharpe, 2008. "Least Median of Squares Estimation by Optimization Heuristics with an Application to the CAPM and Multi Factor Models," Working Papers 006, COMISEF.
    41. Khavul, Susanna & Pérez-Nordtvedt, Liliana & Wood, Eric, 2010. "Organizational entrainment and international new ventures from emerging markets," Journal of Business Venturing, Elsevier, vol. 25(1), pages 104-119, January.
    42. Onder, A. Ozlem & Zaman, Asad, 2005. "Robust tests for normality of errors in regression models," Economics Letters, Elsevier, vol. 86(1), pages 63-68, January.
    43. Hasan, Hamid, 2013. "Capabilities vis-a-vis Happiness: Evidence from Pakistan," MPRA Paper 44892, University Library of Munich, Germany.
    44. Islam, Tanweer ul, 2008. "Normality Testing- A New Direction," MPRA Paper 16452, University Library of Munich, Germany.
    45. Soukissian, Takvor H. & Karathanasi, Flora E., 2016. "On the use of robust regression methods in wind speed assessment," Renewable Energy, Elsevier, vol. 99(C), pages 1287-1298.
    46. C. Colombier, 2009. "Growth effects of fiscal policies: an application of robust modified M-estimator," Applied Economics, Taylor & Francis Journals, vol. 41(7), pages 899-912.
    47. Hella, Heikki, 2003. "On robust ESACF identification of mixed ARIMA models," Bank of Finland Scientific Monographs, Bank of Finland, volume 0, number sm2003_027, July.

  4. Christmann, Andreas & Rousseeuw, Peter J., 1999. "Measuring overlap in logistic regression," Technical Reports 1999,25, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

    Cited by:

    1. Christmann, Andreas, 2004. "Regression depth and support vector machine," Technical Reports 2004,54, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Fijorek, Kamil & Sokolowski, Andrzej, 2012. "Separation-Resistant and Bias-Reduced Logistic Regression: STATISTICA Macro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(c02).
    3. Christmann, Andreas, 2004. "On a strategy to develop robust and simple tariffs from motor vehicle insurance data," Technical Reports 2004,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    4. Christmann, Andreas & Steinwart, Ingo, 2003. "On robustness properties of convex risk minimization methods for pattern recognition," Technical Reports 2003,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.

Articles

  1. Rousseeuw, Peter & Perrotta, Domenico & Riani, Marco & Hubert, Mia, 2019. "Robust Monitoring of Time Series with Application to Fraud Detection," Econometrics and Statistics, Elsevier, vol. 9(C), pages 108-121.

    Cited by:

    1. Vanessa Berenguer-Rico & Søren Johansen & Bent Nielsen, 2019. "Models where the Least Trimmed Squares and Least Median of Squares estimators are maximum likelihood," Economics Papers 2019-W05, Economics Group, Nuffield College, University of Oxford.
    2. Lila, Maurício Franca & Meira, Erick & Cyrino Oliveira, Fernando Luiz, 2022. "Forecasting unemployment in Brazil: A robust reconciliation approach using hierarchical data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    3. Francesca Torti & Marco Riani & Gianluca Morelli, 2021. "Semiautomatic robust regression clustering of international trade data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 863-894, September.
    4. Maria E. Frey & Hans C. Petersen & Oke Gerke, 2020. "Nonparametric Limits of Agreement for Small to Moderate Sample Sizes: A Simulation Study," Stats, MDPI, vol. 3(3), pages 1-13, August.
    5. Lucio Barabesi & Andrea Cerioli & Domenico Perrotta, 2021. "Forum on Benford’s law and statistical methods for the detection of frauds," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(3), pages 767-778, September.
    6. Luca Barbaglia & Christophe Croux & Ines Wilms, 2022. "Detecting Anti-dumping Circumvention: A Network Approach," Papers 2207.05394, arXiv.org.
    7. Grossi, Luigi & Nan, Fany, 2019. "Robust forecasting of electricity prices: Simulations, models and the impact of renewable sources," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 305-318.

  2. Raymaekers, Jakob & Rousseeuw, Peter, 2019. "A generalized spatial sign covariance matrix," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 94-111.

    Cited by:

    1. Majumdar, Subhabrata & Chatterjee, Snigdhansu, 2022. "On weighted multivariate sign functions," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    2. Bernard, Gaspard & Verdebout, Thomas, 2024. "On some multivariate sign tests for scatter matrix eigenvalues," Econometrics and Statistics, Elsevier, vol. 29(C), pages 252-260.

  3. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.

    Cited by:

    1. Stanislav Nagy & Houyem Demni & Davide Buttarazzi & Giovanni C. Porzio, 2024. "Theory of angular depth for classification of directional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 627-662, September.
    2. Amparo Baíllo & Javier Cárcamo & Konstantin Getman, 2019. "New distance measures for classifying X-ray astronomy data into stellar classes," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(2), pages 531-557, June.
    3. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    4. Antonio Elías & Raúl Jiménez & J. E. Yukich, 2023. "Localization processes for functional data analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 17(2), pages 485-517, June.
    5. Vencalek, Ondrej & Pokotylo, Oleksii, 2018. "Depth-weighted Bayes classification," Computational Statistics & Data Analysis, Elsevier, vol. 123(C), pages 1-12.
    6. Amovin-Assagba, Martial & Gannaz, Irène & Jacques, Julien, 2022. "Outlier detection in multivariate functional data through a contaminated mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    7. Elías, Antonio & Jiménez, Raúl & Shang, Han Lin, 2022. "On projection methods for functional time series forecasting," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    8. Beatriz Sinova & Stefan Van Aelst & Pedro Terán, 2021. "M-estimators and trimmed means: from Hilbert-valued to fuzzy set-valued data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 267-288, June.
    9. Guillermo Vinue & Irene Epifanio, 2021. "Robust archetypoids for anomaly detection in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 437-462, June.

  4. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Rejoinder to ‘multivariate functional outlier detection’," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 269-277, July.

    Cited by:

    1. Qiu, Zhiping & Chen, Jianwei & Zhang, Jin-Ting, 2021. "Two-sample tests for multivariate functional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. Weiyi Xie & Sebastian Kurtek & Karthik Bharath & Ying Sun, 2017. "A Geometric Approach to Visualization of Variability in Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 979-993, July.
    3. Moritz Herrmann & Fabian Scheipl, 2021. "A Geometric Perspective on Functional Outlier Detection," Stats, MDPI, vol. 4(4), pages 1-41, November.
    4. Ana Arribas-Gil & Juan Romo, 2015. "Discussion of “Multivariate functional outlier detection”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 263-267, July.
    5. Stanislav Nagy & Houyem Demni & Davide Buttarazzi & Giovanni C. Porzio, 2024. "Theory of angular depth for classification of directional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 627-662, September.
    6. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    7. Martha Bohorquez & Ramón Giraldo & Jorge Mateu, 2016. "Optimal sampling for spatial prediction of functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 39-54, March.
    8. Zhu, Tianming & Zhang, Jin-Ting & Cheng, Ming-Yen, 2022. "One-way MANOVA for functional data via Lawley–Hotelling trace test," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    9. P. Navarro-Esteban & J. A. Cuesta-Albertos, 2021. "High-dimensional outlier detection using random projections," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 908-934, December.
    10. Carlo Sguera & Sara López-Pintado, 2021. "A notion of depth for sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 630-649, September.
    11. Dai, Wenlin & Genton, Marc G., 2019. "Directional outlyingness for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 50-65.
    12. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    13. Kalogridis, Ioannis & Van Aelst, Stefan, 2019. "Robust functional regression based on principal components," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 393-415.
    14. Benjamin Avanzi & Mark Lavender & Greg Taylor & Bernard Wong, 2022. "Detection and treatment of outliers for multivariate robust loss reserving," Papers 2203.03874, arXiv.org, revised Jun 2023.
    15. Łukasz Smaga & Hidetoshi Matsui, 2018. "A note on variable selection in functional regression via random subspace method," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 455-477, August.
    16. Boente, Graciela & Parada, Daniela, 2023. "Robust estimation for functional quadratic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    17. Francesca Ieva & Anna Paganoni, 2015. "Discussion of “multivariate functional outlier detection” by M. Hubert, P. Rousseeuw and P. Segaert," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 217-221, July.
    18. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    19. Kuhnt, Sonja & Rehage, André, 2016. "An angle-based multivariate functional pseudo-depth for shape outlier detection," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 325-340.
    20. Andrea Cerioli & Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2018. "The power of monitoring: how to make the most of a contaminated multivariate sample," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 559-587, December.
    21. Archimbaud, Aurore & Boulfani, Fériel & Gendre, Xavier & Nordhausen, Klaus & Ruiz-Gazen, Anne & Virta, Joni, 2021. "ICS for multivariate functional anomaly detection with applications to predictive maintenance and quality control," TSE Working Papers 21-1182, Toulouse School of Economics (TSE), revised Mar 2022.
    22. Dai, Wenlin & Mrkvička, Tomáš & Sun, Ying & Genton, Marc G., 2020. "Functional outlier detection and taxonomy by sequential transformations," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    23. Amovin-Assagba, Martial & Gannaz, Irène & Jacques, Julien, 2022. "Outlier detection in multivariate functional data through a contaminated mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    24. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    25. Graciela Boente & Matías Salibián-Barrera, 2021. "Robust functional principal components for sparse longitudinal data," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 159-188, August.
    26. Gianluca Trotta & Stefania Cacace & Quirico Semeraro, 2022. "Process optimization via confidence region: a case study from micro-injection molding," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2045-2057, October.
    27. Qiu, Zhiping & Fan, Jiangyuan & Zhang, Jin-Ting & Chen, Jianwei, 2024. "Tests for equality of several covariance matrix functions for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    28. Jorge R. Sosa Donoso & Miguel Flores & Salvador Naya & Javier Tarrío-Saavedra, 2023. "Local Correlation Integral Approach for Anomaly Detection Using Functional Data," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    29. Oluwasegun Taiwo Ojo & Antonio Fernández Anta & Rosa E. Lillo & Carlo Sguera, 2022. "Detecting and classifying outliers in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 725-760, September.
    30. Virta, Joni & Li, Bing & Nordhausen, Klaus & Oja, Hannu, 2020. "Independent component analysis for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    31. Kalogridis, Ioannis, 2024. "Robust and adaptive functional logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    32. Ojo, Oluwasegun Taiwo & Fernández Anta, Antonio & Genton, Marc G., 2022. "Multivariate Functional Outlier Detection using the FastMUOD Indices," DES - Working Papers. Statistics and Econometrics. WS 35665, Universidad Carlos III de Madrid. Departamento de Estadística.
    33. Davy Paindaveine & Germain Van Bever, 2015. "Discussion of “Multivariate Functional Outlier Detection”, by Mia Hubert, Peter Rousseeuw and Pieter Segaert," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 223-231, July.
    34. Martínez-Hernández, Israel & Genton, Marc G. & González-Farías, Graciela, 2019. "Robust depth-based estimation of the functional autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 66-79.
    35. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.
    36. Guillermo Vinue & Irene Epifanio, 2021. "Robust archetypoids for anomaly detection in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 437-462, June.
    37. Xiaohui Liu & Karl Mosler & Pavlo Mozharovskyi, 2017. "Fast computation of Tukey trimmed regions and median in dimension p > 2," Working Papers 2017-71, Center for Research in Economics and Statistics.
    38. Stephane Heritier & Maria-Pia Victoria-Feser, 2018. "Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 595-602, December.
    39. Daniela De Canditiis & Italia De Feis, 2021. "Anomaly Detection in Multichannel Data Using Sparse Representation in RADWT Frames," Mathematics, MDPI, vol. 9(11), pages 1-26, June.

  5. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.

    Cited by:

    1. Qiu, Zhiping & Chen, Jianwei & Zhang, Jin-Ting, 2021. "Two-sample tests for multivariate functional data with applications," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).
    2. Weiyi Xie & Sebastian Kurtek & Karthik Bharath & Ying Sun, 2017. "A Geometric Approach to Visualization of Variability in Functional Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 979-993, July.
    3. Moritz Herrmann & Fabian Scheipl, 2021. "A Geometric Perspective on Functional Outlier Detection," Stats, MDPI, vol. 4(4), pages 1-41, November.
    4. Ana Arribas-Gil & Juan Romo, 2015. "Discussion of “Multivariate functional outlier detection”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 263-267, July.
    5. Stanislav Nagy & Houyem Demni & Davide Buttarazzi & Giovanni C. Porzio, 2024. "Theory of angular depth for classification of directional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 18(3), pages 627-662, September.
    6. Alvarez, Agustín & Boente, Graciela & Kudraszow, Nadia, 2019. "Robust sieve estimators for functional canonical correlation analysis," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 46-62.
    7. Martha Bohorquez & Ramón Giraldo & Jorge Mateu, 2016. "Optimal sampling for spatial prediction of functional data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 25(1), pages 39-54, March.
    8. Zhu, Tianming & Zhang, Jin-Ting & Cheng, Ming-Yen, 2022. "One-way MANOVA for functional data via Lawley–Hotelling trace test," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
    9. P. Navarro-Esteban & J. A. Cuesta-Albertos, 2021. "High-dimensional outlier detection using random projections," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 908-934, December.
    10. Carlo Sguera & Sara López-Pintado, 2021. "A notion of depth for sparse functional data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 630-649, September.
    11. Dai, Wenlin & Genton, Marc G., 2019. "Directional outlyingness for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 50-65.
    12. Davy Paindaveine & Germain Van Bever, 2017. "Halfspace Depths for Scatter, Concentration and Shape Matrices," Working Papers ECARES ECARES 2017-19, ULB -- Universite Libre de Bruxelles.
    13. Kalogridis, Ioannis & Van Aelst, Stefan, 2019. "Robust functional regression based on principal components," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 393-415.
    14. Benjamin Avanzi & Mark Lavender & Greg Taylor & Bernard Wong, 2022. "Detection and treatment of outliers for multivariate robust loss reserving," Papers 2203.03874, arXiv.org, revised Jun 2023.
    15. Łukasz Smaga & Hidetoshi Matsui, 2018. "A note on variable selection in functional regression via random subspace method," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(3), pages 455-477, August.
    16. Boente, Graciela & Parada, Daniela, 2023. "Robust estimation for functional quadratic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    17. Francesca Ieva & Anna Paganoni, 2015. "Discussion of “multivariate functional outlier detection” by M. Hubert, P. Rousseeuw and P. Segaert," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 217-221, July.
    18. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    19. Kuhnt, Sonja & Rehage, André, 2016. "An angle-based multivariate functional pseudo-depth for shape outlier detection," Journal of Multivariate Analysis, Elsevier, vol. 146(C), pages 325-340.
    20. Andrea Cerioli & Marco Riani & Anthony C. Atkinson & Aldo Corbellini, 2018. "The power of monitoring: how to make the most of a contaminated multivariate sample," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 559-587, December.
    21. Archimbaud, Aurore & Boulfani, Fériel & Gendre, Xavier & Nordhausen, Klaus & Ruiz-Gazen, Anne & Virta, Joni, 2021. "ICS for multivariate functional anomaly detection with applications to predictive maintenance and quality control," TSE Working Papers 21-1182, Toulouse School of Economics (TSE), revised Mar 2022.
    22. Dai, Wenlin & Mrkvička, Tomáš & Sun, Ying & Genton, Marc G., 2020. "Functional outlier detection and taxonomy by sequential transformations," Computational Statistics & Data Analysis, Elsevier, vol. 149(C).
    23. Amovin-Assagba, Martial & Gannaz, Irène & Jacques, Julien, 2022. "Outlier detection in multivariate functional data through a contaminated mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    24. Rennie, Nicola & Cleophas, Catherine & Sykulski, Adam M. & Dost, Florian, 2021. "Identifying and responding to outlier demand in revenue management," European Journal of Operational Research, Elsevier, vol. 293(3), pages 1015-1030.
    25. Graciela Boente & Matías Salibián-Barrera, 2021. "Robust functional principal components for sparse longitudinal data," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 159-188, August.
    26. Gianluca Trotta & Stefania Cacace & Quirico Semeraro, 2022. "Process optimization via confidence region: a case study from micro-injection molding," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 2045-2057, October.
    27. Qiu, Zhiping & Fan, Jiangyuan & Zhang, Jin-Ting & Chen, Jianwei, 2024. "Tests for equality of several covariance matrix functions for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    28. Jorge R. Sosa Donoso & Miguel Flores & Salvador Naya & Javier Tarrío-Saavedra, 2023. "Local Correlation Integral Approach for Anomaly Detection Using Functional Data," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    29. Oluwasegun Taiwo Ojo & Antonio Fernández Anta & Rosa E. Lillo & Carlo Sguera, 2022. "Detecting and classifying outliers in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 725-760, September.
    30. Alicia Nieto-Reyes & Juan Cuesta-Albertos, 2015. "M. Hubert, P. Rousseeuw and P. Segaert: Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 237-243, July.
    31. Virta, Joni & Li, Bing & Nordhausen, Klaus & Oja, Hannu, 2020. "Independent component analysis for multivariate functional data," Journal of Multivariate Analysis, Elsevier, vol. 176(C).
    32. Kalogridis, Ioannis, 2024. "Robust and adaptive functional logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
    33. Ojo, Oluwasegun Taiwo & Fernández Anta, Antonio & Genton, Marc G., 2022. "Multivariate Functional Outlier Detection using the FastMUOD Indices," DES - Working Papers. Statistics and Econometrics. WS 35665, Universidad Carlos III de Madrid. Departamento de Estadística.
    34. Davy Paindaveine & Germain Van Bever, 2015. "Discussion of “Multivariate Functional Outlier Detection”, by Mia Hubert, Peter Rousseeuw and Pieter Segaert," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 223-231, July.
    35. Martínez-Hernández, Israel & Genton, Marc G. & González-Farías, Graciela, 2019. "Robust depth-based estimation of the functional autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 66-79.
    36. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.
    37. Guillermo Vinue & Irene Epifanio, 2021. "Robust archetypoids for anomaly detection in big functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 437-462, June.
    38. Kyunghee Han & Pantelis Z Hadjipantelis & Jane-Ling Wang & Michael S Kramer & Seungmi Yang & Richard M Martin & Hans-Georg Müller, 2018. "Functional principal component analysis for identifying multivariate patterns and archetypes of growth, and their association with long-term cognitive development," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-18, November.
    39. Xiaohui Liu & Karl Mosler & Pavlo Mozharovskyi, 2017. "Fast computation of Tukey trimmed regions and median in dimension p > 2," Working Papers 2017-71, Center for Research in Economics and Statistics.
    40. Stephane Heritier & Maria-Pia Victoria-Feser, 2018. "Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 595-602, December.
    41. Daniela De Canditiis & Italia De Feis, 2021. "Anomaly Detection in Multichannel Data Using Sparse Representation in RADWT Frames," Mathematics, MDPI, vol. 9(11), pages 1-26, June.

  6. Peter Rousseeuw & Wannes den Bossche, 2015. "Comments on: Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 473-477, September.

    Cited by:

    1. Leung, Andy & Yohai, Victor & Zamar, Ruben, 2017. "Multivariate location and scatter matrix estimation under cellwise and casewise contamination," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 59-76.

  7. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.

    Cited by:

    1. Jakob Raymaekers & Peter J. Rousseeuw & Iwein Vranckx, 2018. "Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample”," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 589-594, December.
    2. Claudio Agostinelli & Andy Leung & Victor Yohai & Ruben Zamar, 2015. "Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(3), pages 441-461, September.
    3. Tarr, G. & Müller, S. & Weber, N.C., 2016. "Robust estimation of precision matrices under cellwise contamination," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 404-420.

  8. Hubert, Mia & Rousseeuw, Peter & Verdonck, Tim, 2009. "Robust PCA for skewed data and its outlier map," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2264-2274, April.

    Cited by:

    1. Boudt, Kris & Croux, Christophe, 2010. "Robust M-estimation of multivariate GARCH models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2459-2469, November.
    2. Osipenko, Maria, 2021. "Directional assessment of traffic flow extremes," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 353-369.
    3. Verpoorten Marijke, 2012. "The Intensity of the Rwandan Genocide: Measures from the Gacaca Records," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 18(1), pages 1-26, April.
    4. Debruyne, Michiel & Hubert, Mia & Van Horebeek, Johan, 2010. "Detecting influential observations in Kernel PCA," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3007-3019, December.
    5. Huang, Xiaolin & Shi, Lei & Pelckmans, Kristiaan & Suykens, Johan A.K., 2014. "Asymmetric ν-tube support vector regression," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 371-382.
    6. Iaci, Ross & Sriram, T.N., 2013. "Robust multivariate association and dimension reduction using density divergences," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 281-295.
    7. Marianna Succurro, 2017. "Financial Bankruptcy across European Countries," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 9(7), pages 132-146, July.
    8. Boente, Graciela & Pires, Ana M. & Rodrigues, Isabel M., 2010. "Detecting influential observations in principal components and common principal components," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 2967-2975, December.
    9. Václav Plevka & Pieter Segaert & Chris M. J. Tampère & Mia Hubert, 2016. "Analysis of travel activity determinants using robust statistics," Transportation, Springer, vol. 43(6), pages 979-996, November.
    10. Szafranek, Karol, 2021. "Evidence on time-varying inflation synchronization," Economic Modelling, Elsevier, vol. 94(C), pages 1-13.
    11. Stephane Heritier & Maria-Pia Victoria-Feser, 2018. "Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(4), pages 595-602, December.

  9. Pison, Greet & Rousseeuw, Peter J. & Filzmoser, Peter & Croux, Christophe, 2003. "Robust factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 145-172, January.

    Cited by:

    1. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
    2. Hubert, Mia & Dierckx, Goedele & Vanpaemel, Dina, 2013. "Detecting influential data points for the Hill estimator in Pareto-type distributions," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 13-28.
    3. Eichengreen, Barry & Mody, Ashoka & Nedeljkovic, Milan & Sarno, Lucio, 2012. "How the Subprime Crisis went global: Evidence from bank credit default swap spreads," Journal of International Money and Finance, Elsevier, vol. 31(5), pages 1299-1318.
    4. Kim, Hyoung-Moon & Maadooliat, Mehdi & Arellano-Valle, Reinaldo B. & Genton, Marc G., 2016. "Skewed factor models using selection mechanisms," Journal of Multivariate Analysis, Elsevier, vol. 145(C), pages 162-177.
    5. Theodore Metaxas, 2012. "Urban Advantages and Disadvantages in Southeastern Europe: An Appreciation of Industrial Firms by Using Exploratory Factor Analysis," European Research Studies Journal, European Research Studies Journal, vol. 0(2), pages 81-104.
    6. W. Holmes Finch, 2024. "Comparison of Methods for Addressing Outliers in Exploratory Factor Analysis and Impact on Accuracy of Determining the Number of Factors," Stats, MDPI, vol. 7(3), pages 1-21, August.
    7. METAXAS, Theodore, 2011. "Territorial Assets And Firms’ Competitiveness In Southern Europe: Industrial Vs Commercial Firms Using Exploratory Factor Analysis," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 11(1).
    8. Croux, Christophe & Joossens, Kristel, 2005. "Influence of observations on the misclassification probability in quadratic discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 96(2), pages 384-403, October.
    9. Anthony C. Atkinson & Marco Riani & Andrea Cerioli, 2018. "Cluster detection and clustering with random start forward searches," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 777-798, April.
    10. Théodore METAXAS & Marie-Noëlle DUQUENNE, 2017. "Partnerships and development policies for small-medium enterprises in Greece: a CFA approach," Region et Developpement, Region et Developpement, LEAD, Universite du Sud - Toulon Var, vol. 45, pages 131-158.
    11. Alper Sinan & B. Barıs Alkan, 2015. "A useful approach to identify the multicollinearity in the presence of outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 986-993, May.
    12. Joni Virta & Niko Lietzén & Henri Nyberg, 2024. "Robust signal dimension estimation via SURE," Statistical Papers, Springer, vol. 65(5), pages 3007-3038, July.
    13. Ella Roelant & Stefan Aelst & Gert Willems, 2009. "The minimum weighted covariance determinant estimator," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(2), pages 177-204, September.
    14. Kim, Hea-Jung, 2018. "Bayesian hierarchical robust factor analysis models for partially observed sample-selection data," Journal of Multivariate Analysis, Elsevier, vol. 164(C), pages 65-82.
    15. Christophe Croux & Peter Exterkate, 2011. "Sparse and Robust Factor Modelling," Tinbergen Institute Discussion Papers 11-122/4, Tinbergen Institute.
    16. Chen, Yunxiao & Lu, Yan & Moustaki, Irini, 2022. "Detection of two-way outliers in multivariate data and application to cheating detection in educational tests," LSE Research Online Documents on Economics 112499, London School of Economics and Political Science, LSE Library.
    17. Metaxas, Theodore & Duquenne, Marie Noelle, 2015. "Small and Medium Sized Firms’ Competitiveness and Territorial Characteristics by using a MLR approach," MPRA Paper 66848, University Library of Munich, Germany.
    18. Yang, Mingan & Dunson, David B. & Baird, Donna, 2010. "Semiparametric Bayes hierarchical models with mean and variance constraints," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2172-2186, September.
    19. Aleš Toman, 2014. "Robust confirmatory factor analysis based on the forward search algorithm," Statistical Papers, Springer, vol. 55(1), pages 233-252, February.
    20. Deliang Dai, 2020. "Mahalanobis Distances on Factor Model Based Estimation," Econometrics, MDPI, vol. 8(1), pages 1-11, March.
    21. Verdonck, T. & Debruyne, M., 2011. "The influence of individual claims on the chain-ladder estimates: Analysis and diagnostic tool," Insurance: Mathematics and Economics, Elsevier, vol. 48(1), pages 85-98, January.
    22. Metaxas, Theodore & Kallioras, Dimitris, 2013. "Small and medium-sized firms' competitiveness and territorial characteristics/assets: The cases of Bari, Varna and Thessaloniki," MPRA Paper 52446, University Library of Munich, Germany.
    23. Cator, Eric A. & Lopuhaä, Hendrik P., 2010. "Asymptotic expansion of the minimum covariance determinant estimators," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2372-2388, November.
    24. Unkel, S. & Trendafilov, N.T., 2010. "A majorization algorithm for simultaneous parameter estimation in robust exploratory factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3348-3358, December.
    25. Christmann, A. & Van Aelst, S., 2006. "Robust estimation of Cronbach's alpha," Journal of Multivariate Analysis, Elsevier, vol. 97(7), pages 1660-1674, August.
    26. Yi-Hao Kao & Benjamin Van Roy, 2014. "Directed Principal Component Analysis," Operations Research, INFORMS, vol. 62(4), pages 957-972, August.
    27. Angela Montanari & Cinzia Viroli, 2010. "A skew-normal factor model for the analysis of student satisfaction towards university courses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(3), pages 473-487.
    28. Pengfei Zhao & Lingxiang Wei & Dong Pan & Jincheng Yang & Yuchuan Ji, 2023. "Analysis of Key Factors Affecting Low-Carbon Travel Behaviors of Urban Residents in Developing Countries: A Case Study in Zhenjiang, China," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    29. Dupuis Lozeron, E. & Victoria-Feser, M.P., 2010. "Robust estimation of constrained covariance matrices for confirmatory factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3020-3032, December.

  10. Rousseeuw, Peter J. & Christmann, Andreas, 2003. "Robustness against separation and outliers in logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 43(3), pages 315-332, July.

    Cited by:

    1. Christmann, Andreas, 2004. "Regression depth and support vector machine," Technical Reports 2004,54, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    2. Luca Insolia & Ana Kenney & Martina Calovi & Francesca Chiaromonte, 2021. "Robust Variable Selection with Optimality Guarantees for High-Dimensional Logistic Regression," Stats, MDPI, vol. 4(3), pages 1-17, August.
    3. Kweh, Qian Long & Tebourbi, Imen & Lo, Huai-Chun & Huang, Cheng-Tsu, 2022. "CEO compensation and firm performance: Evidence from financially constrained firms," Research in International Business and Finance, Elsevier, vol. 61(C).
    4. Knox, Kris Joseph & Blankmeyer, Eric C. & Trinidad, José A. & Stutzman, J.R., 2009. "Predicting bankruptcy in the Texas nursing facility industry," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(3), pages 1047-1064, August.
    5. Hana Šinkovec & Angelika Geroldinger & Georg Heinze, 2019. "Bring More Data!—A Good Advice? Removing Separation in Logistic Regression by Increasing Sample Size," IJERPH, MDPI, vol. 16(23), pages 1-12, November.
    6. Croux, Christophe & Haesbroeck, Gentiane, 2003. "Implementing the Bianco and Yohai estimator for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 273-295, October.
    7. Christmann, Andreas, 2004. "On a strategy to develop robust and simple tariffs from motor vehicle insurance data," Technical Reports 2004,16, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Elok Heniwati & Angga Hervianto, 2024. "Logistic Regression to Predict BPK's Audit Opinion," Oblik i finansi, Institute of Accounting and Finance, issue 2, pages 118-127, June.
    9. Gerhard Tutz & Jan Gertheiss, 2014. "Rating Scales as Predictors—The Old Question of Scale Level and Some Answers," Psychometrika, Springer;The Psychometric Society, vol. 79(3), pages 357-376, July.
    10. Tutz, Gerhard & Leitenstorfer, Florian, 2006. "Response shrinkage estimators in binary regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2878-2901, June.
    11. Elena Castilla & Abhik Ghosh & Nirian Martin & Leandro Pardo, 2021. "Robust semiparametric inference for polytomous logistic regression with complex survey design," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 701-734, September.
    12. Ying Guan & Guang-Hui Fu, 2022. "A Double-Penalized Estimator to Combat Separation and Multicollinearity in Logistic Regression," Mathematics, MDPI, vol. 10(20), pages 1-19, October.
    13. Jaros³aw Kaczmarek, 2012. "Construction Elements Of Bankruptcy Prediction Models In Multi–Dimensional Early Warning Systems," Polish Journal of Management Studies, Czestochowa Technical University, Department of Management, vol. 5(1), pages 136-149, June.

  11. Rousseeuw, Peter J. & Verboven, Sabine, 2002. "Robust estimation in very small samples," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 741-758, October.

    Cited by:

    1. Huiming Zhang & Haoyu Wei & Guang Cheng, 2023. "Tight Non-asymptotic Inference via Sub-Gaussian Intrinsic Moment Norm," Papers 2303.07287, arXiv.org, revised Jan 2024.
    2. Patricia A Ryan & Brian W Kirk & Chad W Euler & Raymond Schuch & Vincent A Fischetti, 2007. "Novel Algorithms Reveal Streptococcal Transcriptomes and Clues about Undefined Genes," PLOS Computational Biology, Public Library of Science, vol. 3(7), pages 1-18, July.
    3. Hampel, Frank & Hennig, Christian & Ronchetti, Elvezio, 2011. "A smoothing principle for the Huber and other location M-estimators," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 324-337, January.

  12. Van Aelst, Stefan & Rousseeuw, Peter J. & Hubert, Mia & Struyf, Anja, 2002. "The Deepest Regression Method," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 138-166, April.

    Cited by:

    1. Wellmann, Robin & Müller, Christine H., 2010. "Tests for multiple regression based on simplicial depth," Journal of Multivariate Analysis, Elsevier, vol. 101(4), pages 824-838, April.
    2. Ryan Cumings-Menon, 2022. "Differentially Private Estimation via Statistical Depth," Papers 2207.12602, arXiv.org.
    3. Wellmann, Robin & Müller, Christine H., 2010. "Depth notions for orthogonal regression," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2358-2371, November.
    4. Stephan Morgenthaler, 2007. "A survey of robust statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 271-293, February.
    5. Neykov, N.M. & Čížek, P. & Filzmoser, P. & Neytchev, P.N., 2012. "The least trimmed quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1757-1770.
    6. Yijun Zuo, 2020. "Depth Induced Regression Medians and Uniqueness," Stats, MDPI, vol. 3(2), pages 1-13, April.
    7. Wellmann, Robin & Harmand, Peter & Müller, Christine H., 2009. "Distribution-free tests for polynomial regression based on simplicial depth," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 622-635, April.
    8. Wellmann, R. & Katina, S. & Muller, Ch.H., 2007. "Calculation of simplicial depth estimators for polynomial regression with applications," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5025-5040, June.
    9. Ursula Gather & Karen Schettlinger & Roland Fried, 2006. "Online signal extraction by robust linear regression," Computational Statistics, Springer, vol. 21(1), pages 33-51, March.
    10. Victor Chernozhukov & Han Hong, 2023. "An MCMC Approach to Classical Estimation," Papers 2301.07782, arXiv.org.
    11. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    12. Stephan Morgenthaler, 2007. "A survey of robust statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 271-293, February.
    13. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
    14. Zuo, Yijun, 2021. "Computation of projection regression depth and its induced median," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    15. Christine Müller, 2011. "Data depth for simple orthogonal regression with application to crack orientation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 74(2), pages 135-165, September.
    16. Zuo, Yijun, 2024. "Non-asymptotic robustness analysis of regression depth median," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    17. Debruyne, M. & Hubert, M. & Portnoy, S. & Vanden Branden, K., 2008. "Censored depth quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1604-1614, January.
    18. Müller, Christine H., 2005. "Depth estimators and tests based on the likelihood principle with application to regression," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 153-181, July.

  13. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April.
    See citations under working paper version above.
  14. Christmann, Andreas & Rousseeuw, Peter J., 2001. "Measuring overlap in binary regression," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 65-75, July.

    Cited by:

    1. Pavlo Mozharovskyi & Karl Mosler & Tatjana Lange, 2015. "Classifying real-world data with the $${ DD}\alpha $$ D D α -procedure," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 287-314, September.
    2. Tatjana Lange & Karl Mosler & Pavlo Mozharovskyi, 2014. "Fast nonparametric classification based on data depth," Statistical Papers, Springer, vol. 55(1), pages 49-69, February.
    3. Boonstra, Philip S. & Barbaro, Ryan P. & Sen, Ananda, 2019. "Default priors for the intercept parameter in logistic regressions," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 245-256.
    4. Cizek, Pavel, 2008. "Robust and Efficient Adaptive Estimation of Binary-Choice Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 687-696, June.
    5. Croux, Christophe & Haesbroeck, Gentiane, 2003. "Implementing the Bianco and Yohai estimator for logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 273-295, October.
    6. Nedret Billor & Asheber Abebe & Asuman Turkmen & Sai Nudurupati, 2008. "Classification Based on Depth Transvariations," Journal of Classification, Springer;The Classification Society, vol. 25(2), pages 249-260, November.
    7. Tutz, Gerhard & Leitenstorfer, Florian, 2006. "Response shrinkage estimators in binary regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2878-2901, June.
    8. Kazakevičiūtė, Agne & Olivo, Malini, 2017. "Point separation in logistic regression on Hilbert space-valued variables," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 84-88.

  15. Croux, Christophe & Dehon, Catherine & Rousseeuw, Peter J. & Aelst, Stefan Van, 2001. "Robust estimation of the conditional median function at elliptical models," Statistics & Probability Letters, Elsevier, vol. 51(4), pages 361-368, February.

    Cited by:

    1. Christophe Croux & Stefan Aelst & Catherine Dehon, 2003. "Bounded influence regression using high breakdown scatter matrices," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(2), pages 265-285, June.
    2. Lanius, Vivian & Gather, Ursula, 2010. "Robust online signal extraction from multivariate time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 966-975, April.
    3. Lanius, Vivian & Gather, Ursula, 2007. "Robust online signal extraction from multivariate time series," Technical Reports 2007,38, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.

  16. Elvira Haezendonck & Greet Pison & Peter Rousseeuw & Anja Struyf & Alain Verbeke, 2000. "The Competitive Advantage of Seaports," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 2(2), pages 69-82, June.
    See citations under working paper version above.
  17. Struyf, Anja & Rousseeuw, Peter J., 2000. "High-dimensional computation of the deepest location," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 415-426, October.

    Cited by:

    1. Xiaohui Liu & Shihua Luo & Yijun Zuo, 2020. "Some results on the computing of Tukey’s halfspace median," Statistical Papers, Springer, vol. 61(1), pages 303-316, February.
    2. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    3. Ryan Cumings-Menon, 2022. "Differentially Private Estimation via Statistical Depth," Papers 2207.12602, arXiv.org.
    4. Rob J. Hyndman & Han Lin Shang, 2008. "Rainbow plots, Bagplots and Boxplots for Functional Data," Monash Econometrics and Business Statistics Working Papers 9/08, Monash University, Department of Econometrics and Business Statistics.
    5. Chanont Banternghansa & Michael W. McCracken, 2009. "Forecast disagreement among FOMC members," Working Papers 2009-059, Federal Reserve Bank of St. Louis.
    6. Marti J. Anderson, 2006. "Distance-Based Tests for Homogeneity of Multivariate Dispersions," Biometrics, The International Biometric Society, vol. 62(1), pages 245-253, March.
    7. Fan Chen & Guy Nason, 2020. "A new method for computing the projection median, its influence curve and techniques for the production of projected quantile plots," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-22, May.
    8. Wilcox, Rand R., 2003. "Inferences based on multiple skipped correlations," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 223-236, October.
    9. Masse, Jean-Claude & Plante, Jean-Francois, 2003. "A Monte Carlo study of the accuracy and robustness of ten bivariate location estimators," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 1-26, February.
    10. Rand Wilcox, 2004. "Inferences Based on a Skipped Correlation Coefficient," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(2), pages 131-143.
    11. Olusoji, Oluwafemi D. & Spaak, Jurg W. & Holmes, Mark & Neyens, Thomas & Aerts, Marc & De Laender, Frederik, 2021. "cyanoFilter: An R package to identify phytoplankton populations from flow cytometry data using cell pigmentation and granularity," Ecological Modelling, Elsevier, vol. 460(C).
    12. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.

  18. Van Aelst, Stefan & Rousseeuw, Peter J., 2000. "Robustness of Deepest Regression," Journal of Multivariate Analysis, Elsevier, vol. 73(1), pages 82-106, April.

    Cited by:

    1. Yijun Zuo, 2020. "Depth Induced Regression Medians and Uniqueness," Stats, MDPI, vol. 3(2), pages 1-13, April.
    2. Zuo, Yijun, 2020. "Large sample properties of the regression depth induced median," Statistics & Probability Letters, Elsevier, vol. 166(C).
    3. Zuo, Yijun, 2021. "Computation of projection regression depth and its induced median," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    4. Mizera, Ivan & Volauf, Milos, 2002. "Continuity of Halfspace Depth Contours and Maximum Depth Estimators: Diagnostics of Depth-Related Methods," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 365-388, November.
    5. Van Aelst, Stefan & Rousseeuw, Peter J. & Hubert, Mia & Struyf, Anja, 2002. "The Deepest Regression Method," Journal of Multivariate Analysis, Elsevier, vol. 81(1), pages 138-166, April.
    6. Zuo, Yijun, 2024. "Non-asymptotic robustness analysis of regression depth median," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    7. Debruyne, M. & Hubert, M. & Portnoy, S. & Vanden Branden, K., 2008. "Censored depth quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1604-1614, January.
    8. Kris Boudt & Derya Caliskan & Christophe Croux, 2011. "Robust explicit estimators of Weibull parameters," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 73(2), pages 187-209, March.
    9. Müller, Christine H., 2005. "Depth estimators and tests based on the likelihood principle with application to regression," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 153-181, July.
    10. Yijun Zuo, 2021. "Robustness of the deepest projection regression functional," Statistical Papers, Springer, vol. 62(3), pages 1167-1193, June.

  19. Struyf, Anja J. & Rousseeuw, Peter J., 1999. "Halfspace Depth and Regression Depth Characterize the Empirical Distribution," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 135-153, April.

    Cited by:

    1. Hassairi, Abdelhamid & Regaieg, Ons, 2008. "On the Tukey depth of a continuous probability distribution," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2308-2313, October.
    2. Christmann, Andreas, 2004. "Regression depth and support vector machine," Technical Reports 2004,54, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    4. Christmann, Andreas & Steinwart, Ingo, 2005. "Consistency and robustness of kernel based regression," Technical Reports 2005,01, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    5. Koshevoy, Gleb A., 2002. "The Tukey Depth Characterizes the Atomic Measure," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 360-364, November.
    6. Hamel, Andreas H. & Kostner, Daniel, 2018. "Cone distribution functions and quantiles for multivariate random variables," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 97-113.
    7. Marc Hallin & Zudi Lu & Davy Paindaveine & Miroslav Siman, 2012. "Local Constant and Local Bilinear Multiple-Output Quantile Regression," Working Papers ECARES ECARES 2012-003, ULB -- Universite Libre de Bruxelles.
    8. Stanislav Nagy, 2021. "Halfspace depth does not characterize probability distributions," Statistical Papers, Springer, vol. 62(3), pages 1135-1139, June.
    9. Petra Laketa & Stanislav Nagy, 2022. "Halfspace depth for general measures: the ray basis theorem and its consequences," Statistical Papers, Springer, vol. 63(3), pages 849-883, June.
    10. Wei, Bei & Lee, Stephen M.S., 2012. "Second-order accuracy of depth-based bootstrap confidence regions," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 112-123.
    11. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    12. Yi He & John H. J. Einmahl, 2017. "Estimation of extreme depth-based quantile regions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 449-461, March.
    13. Gather, Ursula & Davies, P. Laurie, 2004. "Robust Statistics," Papers 2004,20, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    14. Kong, Linglong & Zuo, Yijun, 2010. "Smooth depth contours characterize the underlying distribution," Journal of Multivariate Analysis, Elsevier, vol. 101(9), pages 2222-2226, October.
    15. Christmann, Andreas & Steinwart, Ingo, 2003. "On robustness properties of convex risk minimization methods for pattern recognition," Technical Reports 2003,15, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    16. Gather, Ursula & Fried, Roland & Lanius, Vivian, 2005. "Robust detail-preserving signal extraction," Technical Reports 2005,54, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    17. Schettlinger, Karen & Fried, Roland & Gather, Ursula, 2006. "Robust Filters for Intensive Care Monitoring: Beyond the Running Median," Technical Reports 2006,23, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    18. Lanius, Vivian & Gather, Ursula, 2007. "Robust online signal extraction from multivariate time series," Technical Reports 2007,38, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    19. Bernholt, Thorsten & Nunkesser, Robin & Schettlinger, Karen, 2005. "Computing the Least Quartile Difference Estimator in the Plane," Technical Reports 2005,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    20. Jonas Baillien & Irène Gijbels & Anneleen Verhasselt, 2023. "Flexible asymmetric multivariate distributions based on two-piece univariate distributions," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 159-200, February.
    21. Laketa, Petra & Nagy, Stanislav, 2021. "Reconstruction of atomic measures from their halfspace depth," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    22. Xiaohui Liu & Karl Mosler & Pavlo Mozharovskyi, 2017. "Fast computation of Tukey trimmed regions and median in dimension p > 2," Working Papers 2017-71, Center for Research in Economics and Statistics.

  20. Pison, Greet & Struyf, Anja & Rousseeuw, Peter J., 1999. "Displaying a clustering with CLUSPLOT," Computational Statistics & Data Analysis, Elsevier, vol. 30(4), pages 381-392, June.

    Cited by:

    1. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    2. Schielein, Johannes & Börner, Jan, 2018. "Recent transformations of land-use and land-cover dynamics across different deforestation frontiers in the Brazilian Amazon," Land Use Policy, Elsevier, vol. 76(C), pages 81-94.
    3. Manca, Francesco & Sivakumar, Aruna & Daina, Nicolò & Axsen, Jonn & Polak, John W, 2020. "Modelling the influence of peers’ attitudes on choice behaviour: Theory and empirical application on electric vehicle preferences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 278-298.

  21. Hubert, Mia & Rousseeuw, Peter J., 1998. "The Catline for Deep Regression," Journal of Multivariate Analysis, Elsevier, vol. 66(2), pages 270-296, August.

    Cited by:

    1. Neykov, N.M. & Čížek, P. & Filzmoser, P. & Neytchev, P.N., 2012. "The least trimmed quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1757-1770.
    2. Wellmann, R. & Katina, S. & Muller, Ch.H., 2007. "Calculation of simplicial depth estimators for polynomial regression with applications," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 5025-5040, June.
    3. Müller, Christine H., 2005. "Depth estimators and tests based on the likelihood principle with application to regression," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 153-181, July.

  22. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter, 1997. "Clustering in an Object-Oriented Environment," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 1(i04).

    Cited by:

    1. Tommaso Agasisti & Francesca Ieva & Anna Maria Paganoni, 2017. "Heterogeneity, school-effects and the North/South achievement gap in Italian secondary education: evidence from a three-level mixed model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 157-180, March.
    2. Jörg Weking & Andreas Hein & Markus Böhm & Helmut Krcmar, 2020. "A hierarchical taxonomy of business model patterns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(3), pages 447-468, September.
    3. Renato Cordeiro Amorim & Vladimir Makarenkov & Boris Mirkin, 2020. "Core Clustering as a Tool for Tackling Noise in Cluster Labels," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 143-157, April.
    4. Wen, Xuanhao & Cao, Huajun & Li, Hongcheng & Zheng, Jie & Ge, Weiwei & Chen, Erheng & Gao, Xi & Hon, Bernard, 2022. "A dual energy benchmarking methodology for energy-efficient production planning and operation of discrete manufacturing systems using data mining techniques," Energy, Elsevier, vol. 255(C).
    5. Ma, Zhenjun & Yan, Rui & Nord, Natasa, 2017. "A variation focused cluster analysis strategy to identify typical daily heating load profiles of higher education buildings," Energy, Elsevier, vol. 134(C), pages 90-102.
    6. Karpinska, Lilia & Śmiech, Sławomir, 2021. "Breaking the cycle of energy poverty. Will Poland make it?," Energy Economics, Elsevier, vol. 94(C).
    7. Jesus Gonzalez-Feliu & Joelle Morana & Josep-Maria Salanova Grau & Tai-Yu Ma, 2013. "Design And Scenario Assessment For Collaborative Logistics And Freight Transport Systems," Articles, International Journal of Transport Economics, vol. 40(2).
    8. Frederickson Entila & Xiaowei Han & Akira Mine & Paul Schulze-Lefert & Kenichi Tsuda, 2024. "Commensal lifestyle regulated by a negative feedback loop between Arabidopsis ROS and the bacterial T2SS," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    9. Alexander Platzer, 2013. "Visualization of SNPs with t-SNE," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-6, February.
    10. Hornik, Kurt, 2005. "A CLUE for CLUster Ensembles," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 14(i12).
    11. Beata Gavurova & Ladislav Suhanyi & Martin Rigelský, 2020. "Tourist spending and productivity of economy in OECD countries – research on perspectives of sustainable tourism," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 8(1), pages 983-1000, September.
    12. Albrecht Kauffmann, 2011. "Wirkung kommunaler Investitionen in die Tourismusinfrastruktur am Beispiel Sachsens," Review of Regional Research: Jahrbuch für Regionalwissenschaft, Springer;Gesellschaft für Regionalforschung (GfR), vol. 31(1), pages 57-73, June.
    13. Mohiuddin Ahmed, 2018. "Collective Anomaly Detection Techniques for Network Traffic Analysis," Annals of Data Science, Springer, vol. 5(4), pages 497-512, December.
    14. Kauffmann, Albrecht, 2012. "Delineation of City Regions Based on Commuting Interrelations: The Example of Large Cities in Germany," IWH Discussion Papers 4/2012, Halle Institute for Economic Research (IWH).
    15. Kim, Jaejik & Billard, L., 2011. "A polythetic clustering process and cluster validity indexes for histogram-valued objects," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2250-2262, July.
    16. Jörg Weking & Michael Mandalenakis & Andreas Hein & Sebastian Hermes & Markus Böhm & Helmut Krcmar, 2020. "The impact of blockchain technology on business models – a taxonomy and archetypal patterns," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(2), pages 285-305, June.

  23. Struyf, Anja & Hubert, Mia & Rousseeuw, Peter J., 1997. "Integrating robust clustering techniques in S-PLUS," Computational Statistics & Data Analysis, Elsevier, vol. 26(1), pages 17-37, November.

    Cited by:

    1. Abellanas, Manuel & Claverol, Merce & Hurtado, Ferran, 2007. "Point set stratification and Delaunay depth," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2513-2530, February.
    2. Leisch, Friedrich, 2006. "A toolbox for K-centroids cluster analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 526-544, November.
    3. Ahmed Albatineh & Magdalena Niewiadomska-Bugaj, 2011. "MCS: A Method for Finding the Number of Clusters," Journal of Classification, Springer;The Classification Society, vol. 28(2), pages 184-209, July.
    4. Karina Acosta & Yuri Reina-Aranza, 2023. "Categorías municipales en Colombia: Avanzando hacia un modelo de descentralización asimétrica," Documentos de trabajo sobre Economía Regional y Urbana 321, Banco de la Republica de Colombia.
    5. Ghosh Samiran & Townsend Jeffrey P., 2015. "H-CLAP: hierarchical clustering within a linear array with an application in genetics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 125-141, April.
    6. Dario Krpan & Jonathan E. Booth & Andreea Damien, 2023. "The positive–negative–competence (PNC) model of psychological responses to representations of robots," Nature Human Behaviour, Nature, vol. 7(11), pages 1933-1954, November.
    7. Aloyce R Kaliba & Kizito Mazvimavi & Theresia L Gregory & Frida M Mgonja & Mary Mgonja, 2018. "Factors affecting adoption of improved sorghum varieties in Tanzania under information and capital constraints," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 6(1), pages 1-21, December.
    8. Birgin, E. G. & Martinez, J. M. & Ronconi, D. P., 2003. "Minimization subproblems and heuristics for an applied clustering problem," European Journal of Operational Research, Elsevier, vol. 146(1), pages 19-34, April.
    9. Slaets, Leen & Claeskens, Gerda & Hubert, Mia, 2012. "Phase and amplitude-based clustering for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2360-2374.
    10. Song, Seongjoo & Nicolae, Dan L. & Song, Jongwoo, 2010. "Estimating the mixing proportion in a semiparametric mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 54(10), pages 2276-2283, October.
    11. Pison, Greet & Struyf, Anja & Rousseeuw, Peter J., 1999. "Displaying a clustering with CLUSPLOT," Computational Statistics & Data Analysis, Elsevier, vol. 30(4), pages 381-392, June.

  24. Ruts, Ida & Rousseeuw, Peter J., 1996. "Computing depth contours of bivariate point clouds," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 153-168, November.

    Cited by:

    1. Xiaohui Liu & Shihua Luo & Yijun Zuo, 2020. "Some results on the computing of Tukey’s halfspace median," Statistical Papers, Springer, vol. 61(1), pages 303-316, February.
    2. Grzywińska-Rąpca, Małgorzata, 2023. "Differentiation of European Households Living in Rural Areas Including Subjective Assessments of their Financial and Economic Situation-Comparative Analysis," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2023(4).
    3. Struyf, Anja & Rousseeuw, Peter J., 2000. "High-dimensional computation of the deepest location," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 415-426, October.
    4. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    5. Romanazzi, Mario, 2001. "Influence Function of Halfspace Depth," Journal of Multivariate Analysis, Elsevier, vol. 77(1), pages 138-161, April.
    6. Mosler, Karl & Lange, Tatjana & Bazovkin, Pavel, 2009. "Computing zonoid trimmed regions of dimension d>2," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2500-2510, May.
    7. Cascos, Ignacio & Ochoa, Maicol, 2021. "Expectile depth: Theory and computation for bivariate datasets," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    8. Małgorzata Kobylińska, 2021. "Spatial Diversity of Organic Farming in Poland," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    9. Francesca Fortunato & Laura Anderlucci & Angela Montanari, 2020. "One‐class classification with application to forensic analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1227-1249, November.
    10. Dyckerhoff, Rainer & Mozharovskyi, Pavlo, 2016. "Exact computation of the halfspace depth," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 19-30.
    11. Maicol Ochoa & Ignacio Cascos, 2022. "Data Depth and Multiple Output Regression, the Distorted M -Quantiles Approach," Mathematics, MDPI, vol. 10(18), pages 1-19, September.
    12. López Pintado, Sara, 2005. "Depth-based classification for functional data," DES - Working Papers. Statistics and Econometrics. WS ws055611, Universidad Carlos III de Madrid. Departamento de Estadística.
    13. Zuo, Yijun & Serfling, Robert, 2000. "Nonparametric Notions of Multivariate "Scatter Measure" and "More Scattered" Based on Statistical Depth Functions," Journal of Multivariate Analysis, Elsevier, vol. 75(1), pages 62-78, October.
    14. Hamel, Andreas H. & Kostner, Daniel, 2022. "Computation of quantile sets for bivariate ordered data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    15. Małgorzata Kobylińska, 2018. "Concept of Observation Depth Measure in the Statistical Analysis of E-Commerce Data in Enterprises," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 49, pages 515-526.
    16. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.
    17. Struyf, Anja J. & Rousseeuw, Peter J., 1999. "Halfspace Depth and Regression Depth Characterize the Empirical Distribution," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 135-153, April.
    18. Liu, Xiaohui & Zuo, Yijun & Wang, Zhizhong, 2013. "Exactly computing bivariate projection depth contours and median," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 1-11.
    19. Zani, Sergio & Riani, Marco & Corbellini, Aldo, 1998. "Robust bivariate boxplots and multiple outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 257-270, September.
    20. Xiaohui Liu & Karl Mosler & Pavlo Mozharovskyi, 2017. "Fast computation of Tukey trimmed regions and median in dimension p > 2," Working Papers 2017-71, Center for Research in Economics and Statistics.
    21. Chakraborty, Biman & Chaudhuri, Probal, 1999. "A note on the robustness of multivariate medians," Statistics & Probability Letters, Elsevier, vol. 45(3), pages 269-276, November.

  25. Rousseeuw, P. J. & Kaufman, L. & Trauwaert, E., 1996. "Fuzzy clustering using scatter matrices," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 135-151, November.

    Cited by:

    1. Cho, Catherine & Kim, Sooyoung & Lee, Jaewook & Lee, Dae-Won, 2006. "A tandem clustering process for multimodal datasets," European Journal of Operational Research, Elsevier, vol. 168(3), pages 998-1008, February.
    2. Daft, Jost & Albers, Sascha, 2015. "An empirical analysis of airline business model convergence," Journal of Air Transport Management, Elsevier, vol. 46(C), pages 3-11.
    3. Ruts, Ida & Rousseeuw, Peter J., 1996. "Computing depth contours of bivariate point clouds," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 153-168, November.
    4. Coppi, Renato & D'Urso, Pierpaolo, 2003. "Three-way fuzzy clustering models for LR fuzzy time trajectories," Computational Statistics & Data Analysis, Elsevier, vol. 43(2), pages 149-177, June.
    5. Berget, Ingunn & Mevik, Bjorn-Helge & Naes, Tormod, 2008. "New modifications and applications of fuzzy C-means methodology," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2403-2418, January.
    6. Francesco Dotto & Alessio Farcomeni & Luis Angel García-Escudero & Agustín Mayo-Iscar, 2017. "A fuzzy approach to robust regression clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 691-710, December.
    7. Pison, Greet & Struyf, Anja & Rousseeuw, Peter J., 1999. "Displaying a clustering with CLUSPLOT," Computational Statistics & Data Analysis, Elsevier, vol. 30(4), pages 381-392, June.

  26. Peter J. Rousseeuw & Ida Ruts, 1996. "Bivariate Location Depth," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 45(4), pages 516-526, December.

    Cited by:

    1. Struyf, Anja & Rousseeuw, Peter J., 2000. "High-dimensional computation of the deepest location," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 415-426, October.
    2. Aloupis, Greg & Cortes, Carmen & Gomez, Francisco & Soss, Michael & Toussaint, Godfried, 2002. "Lower bounds for computing statistical depth," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 223-229, August.
    3. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2015. "Multivariate functional outlier detection," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(2), pages 177-202, July.
    4. Abellanas, Manuel & Claverol, Merce & Hurtado, Ferran, 2007. "Point set stratification and Delaunay depth," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2513-2530, February.
    5. Romanazzi, Mario, 2001. "Influence Function of Halfspace Depth," Journal of Multivariate Analysis, Elsevier, vol. 77(1), pages 138-161, April.
    6. Xiaohui Liu, 2017. "Fast implementation of the Tukey depth," Computational Statistics, Springer, vol. 32(4), pages 1395-1410, December.
    7. Mosler, Karl & Lange, Tatjana & Bazovkin, Pavel, 2009. "Computing zonoid trimmed regions of dimension d>2," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2500-2510, May.
    8. Zuo, Yijun & Lai, Shaoyong, 2011. "Exact computation of bivariate projection depth and the Stahel-Donoho estimator," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1173-1179, March.
    9. Cascos, Ignacio & Ochoa, Maicol, 2021. "Expectile depth: Theory and computation for bivariate datasets," Journal of Multivariate Analysis, Elsevier, vol. 184(C).
    10. Sara López-Pintado & Ying Sun & Juan Lin & Marc Genton, 2014. "Simplicial band depth for multivariate functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 321-338, September.
    11. Małgorzata Kobylińska, 2021. "Spatial Diversity of Organic Farming in Poland," Sustainability, MDPI, vol. 13(16), pages 1-19, August.
    12. Marc Hallin & Davy Paindaveine & Miroslav Šiman, 2010. "Multivariate quantiles and multiple-output regression quantiles: From L1 optimization to halfspace depth," ULB Institutional Repository 2013/127979, ULB -- Universite Libre de Bruxelles.
    13. Dyckerhoff, Rainer & Mozharovskyi, Pavlo, 2016. "Exact computation of the halfspace depth," Computational Statistics & Data Analysis, Elsevier, vol. 98(C), pages 19-30.
    14. Maicol Ochoa & Ignacio Cascos, 2022. "Data Depth and Multiple Output Regression, the Distorted M -Quantiles Approach," Mathematics, MDPI, vol. 10(18), pages 1-19, September.
    15. Messaoud, Amor & Weihs, Claus & Hering, Franz, 2008. "Detection of chatter vibration in a drilling process using multivariate control charts," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3208-3219, February.
    16. Zuo, Yijun & Serfling, Robert, 2000. "Nonparametric Notions of Multivariate "Scatter Measure" and "More Scattered" Based on Statistical Depth Functions," Journal of Multivariate Analysis, Elsevier, vol. 75(1), pages 62-78, October.
    17. Ruts, Ida & Rousseeuw, Peter J., 1996. "Computing depth contours of bivariate point clouds," Computational Statistics & Data Analysis, Elsevier, vol. 23(1), pages 153-168, November.
    18. Messaoud, Amor & Weihs, Claus & Hering, Franz, 2004. "A Nonparametric Multivariate Control Chart Based on Data Depth," Technical Reports 2004,61, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    19. Masse, Jean-Claude & Plante, Jean-Francois, 2003. "A Monte Carlo study of the accuracy and robustness of ten bivariate location estimators," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 1-26, February.
    20. Hamel, Andreas H. & Kostner, Daniel, 2022. "Computation of quantile sets for bivariate ordered data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    21. Małgorzata Kobylińska, 2018. "Concept of Observation Depth Measure in the Statistical Analysis of E-Commerce Data in Enterprises," Collegium of Economic Analysis Annals, Warsaw School of Economics, Collegium of Economic Analysis, issue 49, pages 515-526.
    22. Rand Wilcox, 2004. "Inferences Based on a Skipped Correlation Coefficient," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(2), pages 131-143.
    23. Hering, Franz & Weihs, Claus & Theis, Winfried & Messaoud, Amor, 2004. "Application and Use of Multivariate Control Charts In a BTA Deep Hole Drilling Process," Technical Reports 2004,30, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    24. Mia Hubert & Peter Rousseeuw & Pieter Segaert, 2017. "Multivariate and functional classification using depth and distance," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(3), pages 445-466, September.
    25. Tian, Yahui & Gel, Yulia R., 2019. "Fusing data depth with complex networks: Community detection with prior information," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 99-116.
    26. Struyf, Anja J. & Rousseeuw, Peter J., 1999. "Halfspace Depth and Regression Depth Characterize the Empirical Distribution," Journal of Multivariate Analysis, Elsevier, vol. 69(1), pages 135-153, April.
    27. Mia Hubert & Stephan Van der Veeken, 2010. "Robust classification for skewed data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(4), pages 239-254, December.
    28. Serfling, Robert & Wang, Yunfei, 2016. "On Liu’s simplicial depth and Randles’ interdirections," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 235-247.
    29. Nolan, D., 1999. "On min-max majority and deepest points," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 325-333, July.
    30. Zani, Sergio & Riani, Marco & Corbellini, Aldo, 1998. "Robust bivariate boxplots and multiple outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 28(3), pages 257-270, September.
    31. Chakraborty, Biman & Chaudhuri, Probal, 1999. "A note on the robustness of multivariate medians," Statistics & Probability Letters, Elsevier, vol. 45(3), pages 269-276, November.

  27. Rousseeuw, Peter J. & Hubert, Mia, 1996. "Regression-free and robust estimation of scale for bivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 67-85, January.

    Cited by:

    1. Caliskan, Derya & Croux, Christophe & Gelper, Sarah, 2009. "Efficient and robust scale estimation for trended time series," Statistics & Probability Letters, Elsevier, vol. 79(18), pages 1900-1905, September.
    2. Gelper, Sarah & Schettlinger, Karen & Croux, Christophe & Gather, Ursula, 2007. "Robust online scale estimation in time series : regression-free approach," Technical Reports 2007,17, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Boente, Graciela & Ruiz, Marcelo & Zamar, Ruben H., 2010. "On a robust local estimator for the scale function in heteroscedastic nonparametric regression," Statistics & Probability Letters, Elsevier, vol. 80(15-16), pages 1185-1195, August.

  28. Rousseeuw, Peter J., 1994. "Unconventional features of positive-breakdown estimators," Statistics & Probability Letters, Elsevier, vol. 19(5), pages 417-431, April.

    Cited by:

    1. Visek, Jan Amos, 2000. "On the diversity of estimates," Computational Statistics & Data Analysis, Elsevier, vol. 34(1), pages 67-89, July.
    2. Rousseeuw, Peter J. & Verboven, Sabine, 2002. "Robust estimation in very small samples," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 741-758, October.
    3. Vakili, Kaveh & Schmitt, Eric, 2014. "Finding multivariate outliers with FastPCS," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 54-66.
    4. Gervini, Daniel, 2003. "A robust and efficient adaptive reweighted estimator of multivariate location and scatter," Journal of Multivariate Analysis, Elsevier, vol. 84(1), pages 116-144, January.
    5. Steven P. Ellis, 2000. "Singularity and outliers in linear regression with application to least squares, least squares linear regression," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 121-129.
    6. Jan Víšek, 1996. "Sensitivity analysis of M-estimates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 48(3), pages 469-495, September.

  29. Rousseeuw, Peter J. & Croux, Christophe, 1994. "The bias of k-step M-estimators," Statistics & Probability Letters, Elsevier, vol. 20(5), pages 411-420, August.

    Cited by:

    1. Sukru Acitas & Pelin Kasap & Birdal Senoglu & Olcay Arslan, 2013. "One-step M -estimators: Jones and Faddy's skewed t -distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1545-1560, July.
    2. Schlittgen, Rainer & Schwabe, Rainer, 2001. "An alternative definition of the influence function," Statistics & Probability Letters, Elsevier, vol. 51(2), pages 143-153, January.
    3. Berrendero, José R. & Zamar, Ruben H., 1999. "Global robustness of location and dispersion estimates," Statistics & Probability Letters, Elsevier, vol. 44(1), pages 63-72, August.
    4. Smirnov, Pavel O. & Shevlyakov, Georgy L., 2014. "Fast highly efficient and robust one-step M-estimators of scale based on Qn," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 153-158.
    5. Croux, Christophe & Haesbroeck, Gentiane, 1999. "Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 161-190, November.
    6. Gather, Ursula & Davies, P. Laurie, 2004. "Robust Statistics," Papers 2004,20, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    7. Croux, C. & Dehon, C. & Yadine, A., 2010. "The K-Step Spatial Sign Covariance Matrix," Other publications TiSEM b3c069e5-3f34-475a-9c1b-1, Tilburg University, School of Economics and Management.
    8. Cem Haydaroğlu & Bilal Gümüş, 2022. "Fault Detection in Distribution Network with the Cauchy-M Estimate—RVFLN Method," Energies, MDPI, vol. 16(1), pages 1-18, December.

  30. Rousseeuw, Peter J. & Wagner, Joachim, 1994. "Robust regression with a distributed intercept using least median of squares," Computational Statistics & Data Analysis, Elsevier, vol. 17(1), pages 65-76, January.

    Cited by:

    1. Vincenzo Verardi & Marjorie Gassner & Darwin Ugarte, 2012. "Robustness for dummies," United Kingdom Stata Users' Group Meetings 2012 09, Stata Users Group.
    2. Rodolphe Desbordes & Vincenzo Verardi, 2017. "Foreign Direct Investment and Democracy: A Robust Fixed Effects Approach to a Complex Relationship," Pacific Economic Review, Wiley Blackwell, vol. 22(1), pages 43-82, February.
    3. Huang, Xiaolin & Shi, Lei & Pelckmans, Kristiaan & Suykens, Johan A.K., 2014. "Asymmetric ν-tube support vector regression," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 371-382.
    4. Zaman, Asad & Rousseeuw, Peter J. & Orhan, Mehmet, 2001. "Econometric applications of high-breakdown robust regression techniques," Economics Letters, Elsevier, vol. 71(1), pages 1-8, April.
    5. Peter Winker & Marianna Lyra & Chris Sharpe, 2008. "Least Median of Squares Estimation by Optimization Heuristics with an Application to the CAPM and Multi Factor Models," Working Papers 006, COMISEF.
    6. Hinloopen, Jeroen & Wagenvoort, Rien, 1997. "On the computation and efficiency of a HBP-GM estimator some simulation results," Computational Statistics & Data Analysis, Elsevier, vol. 25(1), pages 1-15, July.

  31. Hossjer, O. & Croux, C. & Rousseeuw, P. J., 1994. "Asymptotics of Generalized S-Estimators," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 148-177, October.

    Cited by:

    1. Roelant, E. & Van Aelst, S. & Croux, C., 2009. "Multivariate generalized S-estimators," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 876-887, May.
    2. Jun, Sung Jae & Pinkse, Joris & Wan, Yuanyuan, 2011. "-Consistent robust integration-based estimation," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 828-846, April.
    3. Ma, Yanyuan & Genton, Marc G., 2001. "Highly Robust Estimation of Dispersion Matrices," Journal of Multivariate Analysis, Elsevier, vol. 78(1), pages 11-36, July.
    4. Farebrother, Richard William, 1997. "The historical development of the linear minimax absolute residual estimation procedure 1786-1960," Computational Statistics & Data Analysis, Elsevier, vol. 24(4), pages 455-466, June.
    5. Hawkins, Douglas M. & Olive, David, 1999. "Applications and algorithms for least trimmed sum of absolute deviations regression," Computational Statistics & Data Analysis, Elsevier, vol. 32(2), pages 119-134, December.
    6. Cizek, P., 2009. "Generalized Methods of Trimmed Moments," Other publications TiSEM 46607f30-95c0-430a-8ef9-2, Tilburg University, School of Economics and Management.
    7. Nunkesser, Robin & Morell, Oliver, 2008. "Evolutionary algorithms for robust methods," Technical Reports 2008,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    8. Agulló, Jose & Croux, Christophe & Van Aelst, Stefan, 2008. "The multivariate least-trimmed squares estimator," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 311-338, March.
    9. Bernholt, Thorsten & Nunkesser, Robin & Schettlinger, Karen, 2007. "Computing the least quartile difference estimator in the plane," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 763-772, October.
    10. Berrendero, José R. & Zamar, Ruben H., 1999. "Global robustness of location and dispersion estimates," Statistics & Probability Letters, Elsevier, vol. 44(1), pages 63-72, August.
    11. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2014. "Strong consistency and robustness of the Forward Search estimator of multivariate location and scatter," Journal of Multivariate Analysis, Elsevier, vol. 126(C), pages 167-183.
    12. Berrendero, José R., 2003. "Uniform strong consistency of robust estimators," Statistics & Probability Letters, Elsevier, vol. 64(2), pages 159-168, August.
    13. Ian L. Dryden & Gary Walker, 1999. "Highly Resistant Regression and Object Matching," Biometrics, The International Biometric Society, vol. 55(3), pages 820-825, September.
    14. W. Ip & Ying Yang & P. Kwan & Y. Kwan, 2003. "Strong convergence rate of the least median absolute estimator in linear regression models," Statistical Papers, Springer, vol. 44(2), pages 183-201, April.
    15. Farnè, Matteo & Vouldis, Angelos T., 2018. "A methodology for automised outlier detection in high-dimensional datasets: an application to euro area banks' supervisory data," Working Paper Series 2171, European Central Bank.
    16. Nunkesser, Robin & Morell, Oliver, 2010. "An evolutionary algorithm for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3242-3248, December.
    17. Croux, Christophe & Ruiz-Gazen, Anne, 2005. "High breakdown estimators for principal components: the projection-pursuit approach revisited," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 206-226, July.
    18. Kudraszow, Nadia L. & Maronna, Ricardo A., 2011. "Estimates of MM type for the multivariate linear model," Journal of Multivariate Analysis, Elsevier, vol. 102(9), pages 1280-1292, October.
    19. Bernholt, Thorsten & Nunkesser, Robin & Schettlinger, Karen, 2005. "Computing the Least Quartile Difference Estimator in the Plane," Technical Reports 2005,51, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    20. Agullo, Jose, 2001. "New algorithms for computing the least trimmed squares regression estimator," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 425-439, June.
    21. Sirkiä, Seija & Taskinen, Sara & Oja, Hannu, 2007. "Symmetrised M-estimators of multivariate scatter," Journal of Multivariate Analysis, Elsevier, vol. 98(8), pages 1611-1629, September.
    22. Soukissian, Takvor H. & Karathanasi, Flora E., 2016. "On the use of robust regression methods in wind speed assessment," Renewable Energy, Elsevier, vol. 99(C), pages 1287-1298.

  32. Rousseeuw, Peter J., 1993. "A resampling design for computing high-breakdown regression," Statistics & Probability Letters, Elsevier, vol. 18(2), pages 125-128, September.

    Cited by:

    1. Prieto, Francisco J., 1997. "Robust covariance matrix estimation and multivariate outlier detection," DES - Working Papers. Statistics and Econometrics. WS 10497, Universidad Carlos III de Madrid. Departamento de Estadística.
    2. Zuo, Yijun & Lai, Shaoyong, 2011. "Exact computation of bivariate projection depth and the Stahel-Donoho estimator," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1173-1179, March.
    3. Shao, Wei & Zuo, Yijun, 2012. "Simulated annealing for higher dimensional projection depth," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4026-4036.
    4. Hadi, Ali S. & Luceno, Alberto, 1997. "Maximum trimmed likelihood estimators: a unified approach, examples, and algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 25(3), pages 251-272, August.
    5. Nolan, D., 1999. "On min-max majority and deepest points," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 325-333, July.

  33. Rousseeuw, Peter J. & van Zomeren, Bert C., 1992. "A comparison of some quick algorithms for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 14(1), pages 107-116, June.

    Cited by:

    1. Giloni, Avi & Simonoff, Jeffrey S. & Sengupta, Bhaskar, 2006. "Robust weighted LAD regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3124-3140, July.
    2. Olive, David J., 2005. "Two simple resistant regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 49(3), pages 809-819, June.

  34. Rousseeuw, Peter J., 1991. "A diagnostic plot for regression outliers and leverage points," Computational Statistics & Data Analysis, Elsevier, vol. 11(1), pages 127-129, January.

    Cited by:

    1. Annalivia Polselli, 2023. "Influence Analysis with Panel Data," Papers 2312.05700, arXiv.org.
    2. Mount, David M. & Netanyahu, Nathan S. & Piatko, Christine D. & Wu, Angela Y. & Silverman, Ruth, 2016. "A practical approximation algorithm for the LTS estimator," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 148-170.

  35. P.J. Rousseeuw & A.M. Leroy, 1988. "A robust scale estimator based on the shortest half," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 42(2), pages 103-116, June.

    Cited by:

    1. Hans DEWACHTER & Deniz ERDEMLIOGLU & Jean-Yves GNABO & Christelle LECOURT, 2013. "The intra-day impact of communication on euro-dollar volatility and jumps," Working Papers of Department of Economics, Leuven ces13.04, KU Leuven, Faculty of Economics and Business (FEB), Department of Economics, Leuven.
    2. A. Christmann & U. Gather & G. Scholz, 1994. "Some properties of the length of the shortest half," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 48(3), pages 209-213, November.
    3. Leclerc J., 2000. "Strong Limiting Behavior Of Two Estimates Of The Mode : The Shorth And The Naive Estimator," Statistics & Risk Modeling, De Gruyter, vol. 18(4), pages 413-428, April.
    4. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2013. "Econometric modeling of exchange rate volatility and jumps," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 16, pages 373-427, Edward Elgar Publishing.
    5. Deniz Erdemlioglu & Sébastien Laurent & Christopher J. Neely, 2015. "Which continuous-time model is most appropriate for exchange rates?," Post-Print hal-01457402, HAL.
    6. Fried, Roland, 2007. "On the robust detection of edges in time series filtering," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 1063-1074, October.
    7. Yi, Chae-Deug, 2020. "Jump probability using volatility periodicity filters in US Dollar/Euro exchange rates," The North American Journal of Economics and Finance, Elsevier, vol. 53(C).
    8. BOUDT, Kris & CROUX, Christophe & LAURENT, Sabéastien, 2011. "Robust estimation of intraweek periodicity in volatility and jump detection," LIDAM Reprints CORE 2411, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Christmann, Andreas, 1998. "On group sequential tests based on robust location and scale estimators in the two-sample problem," Technical Reports 1998,02, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    10. Eric Jondeau & Jérôme Lahaye & Michael Rockinger, 2013. "Estimating the Price Impact of Trades in an High-Frequency Microstructure Model with Jumps," Swiss Finance Institute Research Paper Series 13-47, Swiss Finance Institute, revised Feb 2016.
    11. Olive, David J., 2007. "Prediction intervals for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3115-3122, March.
    12. Gather, Ursula & Schultze, Verena, 1997. "Robust extimation of scale of an exponential distribution," Technical Reports 1997,08, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    13. Jérôme Lahaye & Christopher Neely, 2020. "The Role of Jumps in Volatility Spillovers in Foreign Exchange Markets: Meteor Shower and Heat Waves Revisited," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 410-427, April.
    14. Fried, Roland H., 2003. "Robust filtering of time series with trends," Technical Reports 2003,30, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    15. Schettlinger, Karen & Fried, Roland & Gather, Ursula, 2006. "Robust Filters for Intensive Care Monitoring: Beyond the Running Median," Technical Reports 2006,23, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    16. Fried, Roland & Gather, Ursula, 2007. "On rank tests for shift detection in time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 221-233, September.
    17. Pawlitschko, Jörg, 2001. "Robust estimation of the location parameter from a two-parameter exponential distribution," Technical Reports 2001,36, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    18. Fried, Roland, 2007. "On the robust detection of edges in time series filtering," Technical Reports 2007,20, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    19. Di Bucchianico, A. & Einmahl, J.H.J. & Mushkudiani, N.A., 2001. "Smallest nonparametric tolerance regions," Other publications TiSEM 436f9be2-d0ad-49af-b6df-9, Tilburg University, School of Economics and Management.
    20. Dumitru, Ana-Maria & Urga, Giovanni, 2016. "Jumps and Information Asymmetry in the US Treasury Market," EconStor Preprints 130148, ZBW - Leibniz Information Centre for Economics.
    21. Fried, Roland & Gather, Ursula, 2006. "On rank tests for shift detection in time series," Technical Reports 2006,48, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    22. YI, Chae-Deug, 2023. "Exchange rate volatility and intraday jump probability with periodicity filters using a local robust variance," Finance Research Letters, Elsevier, vol. 55(PA).

  36. Rousseeuw, P. & Daniels, B. & Leroy, A., 1984. "Applying robust regression to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 3(1), pages 67-72, January.

    Cited by:

    1. Fuzi, Mohd Fadzli Mohd & Jemain, Abdul Aziz & Ismail, Noriszura, 2016. "Bayesian quantile regression model for claim count data," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 124-137.
    2. Dalkilic, Turkan Erbay & Tank, Fatih & Kula, Kamile Sanli, 2009. "Neural networks approach for determining total claim amounts in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 236-241, October.
    3. Richardson, Robert & Hartman, Brian, 2018. "Bayesian nonparametric regression models for modeling and predicting healthcare claims," Insurance: Mathematics and Economics, Elsevier, vol. 83(C), pages 1-8.
    4. Kudryavtsev, Andrey A., 2009. "Using quantile regression for rate-making," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 296-304, October.
    5. Pitselis, Georgios, 2008. "Robust regression credibility: The influence function approach," Insurance: Mathematics and Economics, Elsevier, vol. 42(1), pages 288-300, February.

Chapters

  1. Luc Aucremanne & Guy Brys & Peter J Rousseeuw & Anja Struyf & Mia Hubert, 2003. "Inflation, relative prices and nominal rigidities," BIS Papers chapters, in: Bank for International Settlements (ed.), Monetary policy in a changing environment, volume 19, pages 81-105, Bank for International Settlements.
    See citations under working paper version above.Sorry, no citations of chapters recorded.
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