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Christian Hennig

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.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).

    Cited by:

    1. Roy Cerqueti & Antonio Iovanella & Raffaele Mattera, 2024. "Clustering networked funded European research activities through rank-size laws," Annals of Operations Research, Springer, vol. 342(3), pages 1707-1735, November.
    2. Michał Fiedler, 2021. "The Effects of Land Use on Concentrations of Nutrients and Selected Metals in Bottom Sediments and the Risk Assessment for Rivers of the Warta River Catchment, Poland," Land, MDPI, vol. 10(6), pages 1-20, June.
    3. Shan Zhong & David B. Hitchcock, 2024. "Functional clustering of fictional narratives using Vonnegut curves," 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(4), pages 1045-1066, December.
    4. Valentina Masarotto & Guido Masarotto, 2024. "Covariance‐based soft clustering of functional data based on the Wasserstein–Procrustes metric," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(2), pages 485-512, June.
    5. Jihane El Ouadi & Hanae Errousso & Nicolas Malhene & Siham Benhadou & Hicham Medromi, 2022. "A machine-learning based hybrid algorithm for strategic location of urban bundling hubs to support shared public transport," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3215-3258, October.

  2. Christian Hennig & Willi Sauerbrei, 2019. "Exploration of the variability of variable selection based on distances between bootstrap sample results," 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(4), pages 933-963, December.

    Cited by:

    1. Qin, Yichen & Wang, Linna & Li, Yang & Li, Rong, 2023. "Visualization and assessment of model selection uncertainty," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    2. Wang, Lu & Wu, Rui & Ma, WeiChun & Xu, Weiju, 2023. "Examining the volatility of soybean market in the MIDAS framework: The importance of bagging-based weather information," International Review of Financial Analysis, Elsevier, vol. 89(C).

  3. Noah N. N. van Dongen & Johnny B. van Doorn & Quentin F. Gronau & Don van Ravenzwaaij & Rink Hoekstra & Matthias N. Haucke & Daniel Lakens & Christian Hennig & Richard D. Morey & Saskia Homer & Andrew, 2019. "Multiple Perspectives on Inference for Two Simple Statistical Scenarios," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 328-339, March.

    Cited by:

    1. Wagenmakers, Eric-Jan & Sarafoglou, Alexandra & Aarts, Sil Dr. & Albers, Casper J & Algermissen, Johannes & Bahník, Štěpán & van Dongen, Noah N'Djaye Nikolai & Hoekstra, Rink & Moreau, David & van Rav, 2021. "Seven Steps Toward More Transparency in Statistical Practice," MetaArXiv t93cg_v1, Center for Open Science.
    2. Riko Kelter, 2021. "Analysis of type I and II error rates of Bayesian and frequentist parametric and nonparametric two-sample hypothesis tests under preliminary assessment of normality," Computational Statistics, Springer, vol. 36(2), pages 1263-1288, June.
    3. Sander Greenland, 2023. "Connecting simple and precise P‐values to complex and ambiguous realities (includes rejoinder to comments on “Divergence vs. decision P‐values”)," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(3), pages 899-914, September.
    4. Wagenmakers, Eric-Jan & Sarafoglou, Alexandra & Aarts, Sil Dr. & Albers, Casper J & Algermissen, Johannes & Bahník, Štěpán & van Dongen, Noah N'Djaye Nikolai & Hoekstra, Rink & Moreau, David & van Rav, 2021. "Toward More Transparency in Statistical Practice," MetaArXiv t93cg, Center for Open Science.

  4. Daniel Müllensiefen & Christian Hennig & Hedie Howells, 2018. "Using clustering of rankings to explain brand preferences with personality and socio-demographic variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(6), pages 1009-1029, April.

    Cited by:

    1. Pierpaolo D’Urso & Vincenzina Vitale, 2022. "A Kemeny Distance-Based Robust Fuzzy Clustering for Preference Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 600-647, November.
    2. Ferreira, Diogo Cunha & Marques, Rui Cunha & Nunes, Alexandre Morais & Figueira, José Rui, 2021. "Customers satisfaction in pediatric inpatient services: A multiple criteria satisfaction analysis," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    3. Antonella Plaia & Simona Buscemi & Johannes Fürnkranz & Eneldo Loza Mencía, 2022. "Comparing Boosting and Bagging for Decision Trees of Rankings," Journal of Classification, Springer;The Classification Society, vol. 39(1), pages 78-99, March.
    4. Dongyun Nie & Michael Scriney & Xiaoning Liang & Mark Roantree, 2024. "From data acquisition to validation: a complete workflow for predicting individual customer lifetime value," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 321-341, June.

  5. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.

    Cited by:

    1. Fernando Delbianco & Fernando Tohmé, 2023. "What is a relevant control?: An algorithmic proposal," Working Papers 269, Red Nacional de Investigadores en Economía (RedNIE).
    2. Mayo, Deborah, 2024. "Error statistics, Bayes-factor Tests and the Fallacy of Non-exhaustive Alternatives," OSF Preprints tmgqd, Center for Open Science.
    3. Wei-Chao Lin & Ching Chen, 2021. "Novel World University Rankings Combining Academic, Environmental and Resource Indicators," Sustainability, MDPI, vol. 13(24), pages 1-15, December.
    4. Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
    5. Claudia Biancotti & Alfonso Rosolia & Giovanni Veronese & Robert Kirchner & Francois Mouriaux, 2021. "Covid-19 and official statistics: a wakeup call?," Questioni di Economia e Finanza (Occasional Papers) 605, Bank of Italy, Economic Research and International Relations Area.
    6. Francesco De Pretis & Barbara Osimani, 2019. "New Insights in Computational Methods for Pharmacovigilance: E-Synthesis , a Bayesian Framework for Causal Assessment," IJERPH, MDPI, vol. 16(12), pages 1-19, June.
    7. Lutz Bornmann & Julian N. Marewski, 2024. "Opium in science and society: numbers and other quantifications," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5313-5346, September.
    8. Elena Grimaccia & Alessia Naccarato, 2019. "Food Insecurity Individual Experience: A Comparison of Economic and Social Characteristics of the Most Vulnerable Groups in the World," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(1), pages 391-410, May.
    9. Jan Hannig & Hari Iyer, 2022. "Testing for calibration discrepancy of reported likelihood ratios in forensic science," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 267-301, January.
    10. Mayo, Deborah, 2024. "Error statistics, Bayes-factor Tests and the Fallacy of Non-exhaustive Alternatives," OSF Preprints tmgqd_v1, Center for Open Science.
    11. Jingjing He & Wei Wang & Min Huang & Shaohua Wang & Xuefei Guan, 2021. "Bayesian Inference under Small Sample Sizes Using General Noninformative Priors," Mathematics, MDPI, vol. 9(21), pages 1-20, November.
    12. John Deke & Mariel Finucane & Daniel Thal, "undated". "The BASIE (BAyeSian Interpretation of Estimates) Framework for Interpreting Findings from Impact Evaluations: A Practical Guide for Education Researchers," Mathematica Policy Research Reports 5a0d5dff375d42048799878be, Mathematica Policy Research.
    13. Brian H. MacGillivray, 2019. "Null Hypothesis Testing ≠ Scientific Inference: A Critique of the Shaky Premise at the Heart of the Science and Values Debate, and a Defense of Value‐Neutral Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 39(7), pages 1520-1532, July.
    14. Taisei Sugiyama & Nicolas Schweighofer & Jun Izawa, 2023. "Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    15. Helfgott, Ariella & Midgley, Gerald & Chaudhury, Abrar & Vervoort, Joost & Sova, Chase & Ryan, Alex, 2023. "Multi-level participation in integrative, systemic planning: The case of climate adaptation in Ghana," European Journal of Operational Research, Elsevier, vol. 309(3), pages 1201-1217.

  6. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.

    Cited by:

    1. Christian Hennig, 2022. "An empirical comparison and characterisation of nine popular clustering methods," 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(1), pages 201-229, March.
    2. Garcia-Marrero, L.E. & Monmasson, E. & Petrone, G., 2025. "Online real-time robust framework for non-intrusive load monitoring in constrained edge devices," Applied Energy, Elsevier, vol. 378(PA).
    3. Mazo, Gildas & Averyanov, Yaroslav, 2019. "Constraining kernel estimators in semiparametric copula mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 138(C), pages 170-189.
    4. Sugasawa, Shonosuke & Kobayashi, Genya, 2022. "Robust fitting of mixture models using weighted complete estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    5. 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.
    6. Hung Tong & Cristina Tortora, 2024. "Missing Values and Directional Outlier Detection in Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 41(3), pages 480-513, November.
    7. Hung Tong & Cristina Tortora, 2022. "Model-based clustering and outlier detection with missing 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(1), pages 5-30, March.
    8. Šárka Brodinová & Peter Filzmoser & Thomas Ortner & Christian Breiteneder & Maia Rohm, 2019. "Robust and sparse k-means clustering for high-dimensional 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. 13(4), pages 905-932, December.
    9. Alessio Farcomeni & Antonio Punzo, 2020. "Robust model-based clustering with mild and gross outliers," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(4), pages 989-1007, December.
    10. 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.
    11. Oliver M Crook & Claire M Mulvey & Paul D W Kirk & Kathryn S Lilley & Laurent Gatto, 2018. "A Bayesian mixture modelling approach for spatial proteomics," PLOS Computational Biology, Public Library of Science, vol. 14(11), pages 1-29, November.
    12. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    13. Pietro Coretto, 2022. "Estimation and computations for Gaussian mixtures with uniform noise under separation constraints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 427-458, June.

  7. C. Hennig & C. Viroli, 2016. "Quantile-based classifiers," Biometrika, Biometrika Trust, vol. 103(2), pages 435-446.

    Cited by:

    1. Lai, Yuanhao & McLeod, Ian, 2020. "Ensemble quantile classifier," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    2. Timothy I. Cannings & Richard J. Samworth, 2017. "Random-projection ensemble classification," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 959-1035, September.

  8. Christian Hennig & Tim F. Liao, 2013. "How to find an appropriate clustering for mixed-type variables with application to socio-economic stratification," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(3), pages 309-369, May.

    Cited by:

    1. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    2. Jean-Patrick Baudry & Margarida Cardoso & Gilles Celeux & Maria Amorim & Ana Ferreira, 2015. "Enhancing the selection of a model-based clustering with external categorical variables," 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(2), pages 177-196, June.
    3. Clara Dugord & Carine Franc, 2022. "Trajectories and individual determinants of regular cancer screening use over a long period based on data from the French E3N cohort," Post-Print hal-04385507, HAL.
    4. Efthymios Costa & Ioanna Papatsouma & Angelos Markos, 2023. "Benchmarking distance-based partitioning methods for mixed-type 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. 17(3), pages 701-724, September.
    5. Bettina Grün & Sara Dolnicar, 2016. "Response style corrected market segmentation for ordinal data," Marketing Letters, Springer, vol. 27(4), pages 729-741, December.
    6. Abby Flynt & Nema Dean, 2016. "A Survey of Popular R Packages for Cluster Analysis," Journal of Educational and Behavioral Statistics, , vol. 41(2), pages 205-225, April.
    7. Morgan, Grant B. & Hodge, Kari J. & Baggett, Aaron R., 2016. "Latent profile analysis with nonnormal mixtures: A Monte Carlo examination of model selection using fit indices," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 146-161.
    8. Jack Blundell, 2020. "Clusters in UK Self-Employment," CEP Occasional Papers 48, Centre for Economic Performance, LSE.
    9. Elvira Pelle & Roberta Pappadà, 2021. "A clustering procedure for mixed-type data to explore ego network typologies: an application to elderly people living alone in Italy," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(5), pages 1507-1533, December.
    10. Dugord, Clara & Franc, Carine, 2022. "Trajectories and individual determinants of regular cancer screening use over a long period based on data from the French E3N cohort," Social Science & Medicine, Elsevier, vol. 294(C).
    11. 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.
    12. Brittney Goodrich & Jisang Yu & Monte Vandeveer, 2020. "Participation patterns of the rainfall index insurance for pasture, rangeland and forage programme," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(1), pages 29-51, January.
    13. Roberto Mari & Roberto Rocci & Stefano Antonio Gattone, 2020. "Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(1), pages 49-78, March.
    14. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(1), pages 85-113, April.
    15. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    16. Layal Christine Lettry, 2023. "Clustering the Swiss Pension Register," FSES Working Papers 529, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    17. Nivorozhkin, Anton & Promberger, Markus, 2020. "Employment Subsidies for Long-Term Welfare Benefits Recipients: Reconciling Programmes Goals with Needs of Diverging Population Groups," IAB-Discussion Paper 202027, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    18. Sophia Terwiel & John F Rauthmann & Maike Luhmann, 2020. "Using the situational characteristics of the DIAMONDS taxonomy to distinguish sports to more precisely investigate their relation with psychologically relevant variables," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-15, October.
    19. Goodrich, Brittney K. & Goodhue, Rachael E., 2020. "Are All Colonies Created Equal? The Role of Honey Bee Colony Strength in Almond Pollination Contracts," Ecological Economics, Elsevier, vol. 177(C).
    20. Natalia Zdanowska, 2023. "Socio-spatial Inequalities in a Context of "Great Economic Wealth". Case study of neighbourhoods of Luxembourg City," Papers 2307.09251, arXiv.org.
    21. Marcel Raab & Anette Fasang & Aleksi Karhula & Jani Erola, 2014. "Sibling Similarity in Family Formation," Demography, Springer;Population Association of America (PAA), vol. 51(6), pages 2127-2154, December.
    22. Lauren Eyler & Alan Hubbard & Catherine Juillard, 2019. "Optimization and validation of the EconomicClusters model for facilitating global health disparities research: Examples from Cameroon and Ghana," PLOS ONE, Public Library of Science, vol. 14(5), pages 1-22, May.
    23. Balabdaoui, Fadoua & Butucea, Cristina, 2014. "On location mixtures with Pólya frequency components," Statistics & Probability Letters, Elsevier, vol. 95(C), pages 144-149.
    24. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "Erratum to: The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 327-355, July.
    25. Stavros Athanasiadis, 2023. "European Insurance Market Analysis via a Joint Functional Clustering Method," Economics Working Papers 2023-06, University of South Bohemia in Ceske Budejovice, Faculty of Economics.
    26. Bettina Grün & Gertraud Malsiner-Walli & Sylvia Frühwirth-Schnatter, 2022. "How many data clusters are in the Galaxy data set?," 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(2), pages 325-349, June.
    27. Rabea Aschenbruck & Gero Szepannek & Adalbert F. X. Wilhelm, 2023. "Imputation Strategies for Clustering Mixed-Type Data with Missing Values," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 2-24, April.
    28. Utkarsh J. Dang & Antonio Punzo & Paul D. McNicholas & Salvatore Ingrassia & Ryan P. Browne, 2017. "Multivariate Response and Parsimony for Gaussian Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 4-34, April.
    29. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    30. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    31. Madero-Cabib, Ignacio & Fasang, Anette Eva, 2016. "Gendered work-family life courses and financial well-being in retirement," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 27, pages 43-60.
    32. Goodrich, Brittney K. & Yu, Jisang, 2018. "Risk Preferences and the Participation Pattern of Rainfall Index Insurance," 2018 Annual Meeting, August 5-7, Washington, D.C. 273879, Agricultural and Applied Economics Association.
    33. Jonathon J. O’Brien & Michael T. Lawson & Devin K. Schweppe & Bahjat F. Qaqish, 2020. "Suboptimal Comparison of Partitions," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 435-461, July.
    34. Andrea Cerasa, 2016. "Combining homogeneous groups of preclassified observations with application to international trade," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(3), pages 229-259, August.
    35. Andrew Gelman & Christian Hennig, 2017. "Beyond subjective and objective in statistics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(4), pages 967-1033, October.

  9. 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.

    Cited by:

    1. Rickard Sandberg, 2015. "M-estimator based unit root tests in the ESTAR framework," Statistical Papers, Springer, vol. 56(4), pages 1115-1135, November.
    2. Wang, Xiaoguang & Shi, Xinyong, 2014. "Robust estimation for survival partially linear single-index models," Computational Statistics & Data Analysis, Elsevier, vol. 80(C), pages 140-152.
    3. Erxu Pi & Nitin Mantri & Sai Ming Ngai & Hongfei Lu & Liqun Du, 2013. "BP-ANN for Fitting the Temperature-Germination Model and Its Application in Predicting Sowing Time and Region for Bermudagrass," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-11, December.

  10. Pietro Coretto & Christian Hennig, 2010. "A simulation study to compare robust clustering methods based on mixtures," 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(2), pages 111-135, September.

    Cited by:

    1. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    2. Andrea Cerioli & Domenico Perrotta, 2014. "Robust clustering around regression lines with high density regions," 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(1), pages 5-26, March.
    3. Pietro Coretto & Michele La Rocca & Giuseppe Storti, 2020. "Improving Many Volatility Forecasts Using Cross-Sectional Volatility Clusters," JRFM, MDPI, vol. 13(4), pages 1-23, March.
    4. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 227-242, June.
    5. Christian Hennig, 2013. "Discussion of “Model-based clustering with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 455-458, November.
    6. L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2014. "A constrained robust proposal for mixture modeling avoiding spurious solutions," 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(1), pages 27-43, March.
    7. Pietro Coretto, 2022. "Estimation and computations for Gaussian mixtures with uniform noise under separation constraints," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(2), pages 427-458, June.
    8. Nicholas Longford & Pierpaolo D’Urso, 2012. "Mixtures of Autoregressions with an Improper Component for Panel Data," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 341-362, October.

  11. Christian Hennig, 2010. "Methods for merging Gaussian mixture components," 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(1), pages 3-34, April.

    Cited by:

    1. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
    2. Pietro Coretto & Christian Hennig, 2016. "Robust Improper Maximum Likelihood: Tuning, Computation, and a Comparison With Other Methods for Robust Gaussian Clustering," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1648-1659, October.
    3. Ahmed, Murat O. & Walther, Guenther, 2012. "Investigating the multimodality of multivariate data with principal curves," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4462-4469.
    4. Melnykov, Volodymyr, 2016. "Model-based biclustering of clickstream data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 31-45.
    5. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    6. Kim, Daeyoung & Seo, Byungtae, 2014. "Assessment of the number of components in Gaussian mixture models in the presence of multiple local maximizers," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 100-120.
    7. Xuwen Zhu & Volodymyr Melnykov, 2015. "Probabilistic assessment of model-based 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. 9(4), pages 395-422, December.
    8. Seo, Byungtae & Kim, Daeyoung, 2012. "Root selection in normal mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2454-2470.
    9. Zhu, Xuwen & Melnykov, Volodymyr, 2018. "Manly transformation in finite mixture modeling," Computational Statistics & Data Analysis, Elsevier, vol. 121(C), pages 190-208.
    10. Giovanna Menardi, 2016. "A Review on Modal Clustering," International Statistical Review, International Statistical Institute, vol. 84(3), pages 413-433, December.
    11. Chauveau, Didier & Hoang, Vy Thuy Lynh, 2016. "Nonparametric mixture models with conditionally independent multivariate component densities," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 1-16.
    12. Warren C Jochem & Douglas R Leasure & Oliver Pannell & Heather R Chamberlain & Patricia Jones & Andrew J Tatem, 2021. "Classifying settlement types from multi-scale spatial patterns of building footprints," Environment and Planning B, , vol. 48(5), pages 1161-1179, June.
    13. Sahin, Özge & Czado, Claudia, 2022. "Vine copula mixture models and clustering for non-Gaussian data," Econometrics and Statistics, Elsevier, vol. 22(C), pages 136-158.
    14. Christophe Biernacki & Matthieu Marbac & Vincent Vandewalle, 2021. "Gaussian-Based Visualization of Gaussian and Non-Gaussian-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 38(1), pages 129-157, April.
    15. Lavigne, Aurore & Liverani, Silvia, 2024. "Quantifying the uncertainty of partitions for infinite mixture models," Statistics & Probability Letters, Elsevier, vol. 204(C).
    16. Coffey, N. & Hinde, J. & Holian, E., 2014. "Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 14-29.
    17. José E. Chacón, 2020. "The Modal Age of Statistics," International Statistical Review, International Statistical Institute, vol. 88(1), pages 122-141, April.
    18. Álvarez, Adolfo, 2013. "Recombining partitions via unimodality tests," DES - Working Papers. Statistics and Econometrics. WS ws130706, Universidad Carlos III de Madrid. Departamento de Estadística.
    19. Yuhong Wei & Paul McNicholas, 2015. "Mixture model averaging for 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. 9(2), pages 197-217, June.
    20. Semhar Michael & Volodymyr Melnykov, 2016. "An effective strategy for initializing the EM algorithm in finite mixture models," 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. 10(4), pages 563-583, December.
    21. Dong, Aqi & Melnykov, Volodymyr, 2024. "Contaminated Kent mixture model for clustering non-spherical directional data with heavy tails or scatter," Statistics & Probability Letters, Elsevier, vol. 208(C).
    22. Peter Radchenko & Gourab Mukherjee, 2017. "Convex clustering via l 1 fusion penalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1527-1546, November.
    23. Volodymyr Melnykov & Semhar Michael, 2020. "Clustering Large Datasets by Merging K-Means Solutions," Journal of Classification, Springer;The Classification Society, vol. 37(1), pages 97-123, April.
    24. José E. Chacón, 2019. "Mixture model modal 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. 13(2), pages 379-404, June.
    25. Cristina Tortora & Paul D. McNicholas & Ryan P. Browne, 2016. "A mixture of generalized hyperbolic factor analyzers," 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. 10(4), pages 423-440, December.
    26. Branislav Panić & Marko Nagode & Jernej Klemenc & Simon Oman, 2022. "On Methods for Merging Mixture Model Components Suitable for Unsupervised Image Segmentation Tasks," Mathematics, MDPI, vol. 10(22), pages 1-22, November.
    27. Andrea Pastore & Stefano Tonellato, 2013. "A merging algorithm for Gaussian mixture components," Working Papers 2013:04, Department of Economics, University of Venice "Ca' Foscari".
    28. Ray, Surajit & Ren, Dan, 2012. "On the upper bound of the number of modes of a multivariate normal mixture," Journal of Multivariate Analysis, Elsevier, vol. 108(C), pages 41-52.
    29. Marek Śmieja & Magdalena Wiercioch, 2017. "Constrained clustering with a complex cluster structure," 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 493-518, September.
    30. Melnykov, Volodymyr, 2013. "On the distribution of posterior probabilities in finite mixture models with application in clustering," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 175-189.
    31. Alessandro Casa & Luca Scrucca & Giovanna Menardi, 2021. "Better than the best? Answers via model ensemble in density-based 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. 15(3), pages 599-623, September.
    32. Andrea Cerasa, 2016. "Combining homogeneous groups of preclassified observations with application to international trade," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(3), pages 229-259, August.
    33. Shuchismita Sarkar & Volodymyr Melnykov & Rong Zheng, 2020. "Gaussian mixture modeling and model-based clustering under measurement inconsistency," 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. 14(2), pages 379-413, June.
    34. Sangkon Oh & Byungtae Seo, 2023. "Merging Components in Linear Gaussian Cluster-Weighted Models," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 25-51, April.
    35. Christian Hennig, 2013. "Discussion of “Model-based clustering with non-normal mixture distributions” by S. X. Lee and G. J. McLachlan," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(4), pages 455-458, November.
    36. O’Hagan, Adrian & Murphy, Thomas Brendan & Gormley, Isobel Claire & McNicholas, Paul D. & Karlis, Dimitris, 2016. "Clustering with the multivariate normal inverse Gaussian distribution," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 18-30.
    37. Álvarez, Adolfo, 2014. "Recombining partitions from multivariate data: a clustering method on Bayes factors," DES - Working Papers. Statistics and Econometrics. WS ws140804, Universidad Carlos III de Madrid. Departamento de Estadística.

  12. Hennig, Christian, 2008. "Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1154-1176, July.

    Cited by:

    1. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    2. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    3. Tino Werner, 2023. "Quantitative robustness of instance ranking problems," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(2), pages 335-368, April.
    4. Ana Helena Tavares & Jakob Raymaekers & Peter J. Rousseeuw & Paula Brito & Vera Afreixo, 2020. "Clustering genomic words in human DNA using peaks and trends of distributions," 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. 14(1), pages 57-76, March.
    5. C. Ruwet & L. García-Escudero & A. Gordaliza & A. Mayo-Iscar, 2013. "On the breakdown behavior of the TCLUST clustering procedure," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 466-487, September.
    6. Marta Rocchi & Guglielmo Pescatore, 2022. "Modeling narrative features in TV series: coding and clustering analysis," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    7. Anastasia Panori, 2017. "A Tale of Hidden Cities," REGION, European Regional Science Association, vol. 4, pages 19-38.
    8. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, 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.

  13. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.

    Cited by:

    1. Kolluru Mythili & Suresh Vidya, 2020. "A Cluster Analysis on Sustained Global Competitiveness for European Countries," Economics, Sciendo, vol. 8(1), pages 7-22, June.
    2. Aicha Ait Sair & Kamal Kansou & Franck Michaud & Bernard Cathala, 2021. "Multicriteria Definition of Small-Scale Biorefineries Based on a Statistical Classification," Sustainability, MDPI, vol. 13(13), pages 1-18, June.
    3. Coraggio, Luca & Coretto, Pietro, 2023. "Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    4. Jyldyz Djumalieva & Antonio Lima & Cath Sleeman, 2018. "Classifying Occupations According to Their Skill Requirements in Job Advertisements," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-04, Economic Statistics Centre of Excellence (ESCoE).
    5. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    6. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    7. Vincent Audigier & Ndèye Niang, 2023. "Clustering with missing data: which equivalent for Rubin’s rules?," 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(3), pages 623-657, September.
    8. Mohr, Lukas & Burg, Vanessa & Thees, Oliver & Trutnevyte, Evelina, 2019. "Spatial hot spots and clusters of bioenergy combined with socio-economic analysis in Switzerland," Renewable Energy, Elsevier, vol. 140(C), pages 840-851.
    9. Jonatan A. González & Francisco J. Rodríguez-Cortés & Elvira Romano & Jorge Mateu, 2021. "Classification of Events Using Local Pair Correlation Functions for Spatial Point Patterns," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(4), pages 538-559, December.
    10. Obal, Thalita Monteiro & de Souza, Jovani Taveira & de Jesus, Rômulo Henrique Gomes & de Francisco, Antonio Carlos, 2023. "Biogascluster: A clustering algorithm to identify potential partnerships between agribusiness properties," Renewable Energy, Elsevier, vol. 206(C), pages 982-993.
    11. Han Yu & Brian Chapman & Arianna Di Florio & Ellen Eischen & David Gotz & Mathews Jacob & Rachael Hageman Blair, 2019. "Bootstrapping estimates of stability for clusters, observations and model selection," Computational Statistics, Springer, vol. 34(1), pages 349-372, March.
    12. Terra A. Schall & King-Lun Li & Xiguang Qi & Brian T. Lee & William J. Wright & Erin E. Alpaugh & Rachel J. Zhao & Jianwei Liu & Qize Li & Bo Zeng & Lirong Wang & Yanhua H. Huang & Oliver M. Schlüter , 2024. "Temporal dynamics of nucleus accumbens neurons in male mice during reward seeking," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    13. Michaël Lainé, 2016. "The heterogeneity of animal spirits: a first taxonomy of entrepreneurs with regard to investment expectations," Post-Print hal-01744745, HAL.
    14. Ali Ferjani & Albert Zimmermann, 2013. "Modelling structural-change-related shifts in labour input in the agent-based sector model SWISSland," Journal of Socio-Economics in Agriculture (Until 2015: Yearbook of Socioeconomics in Agriculture), Swiss Society for Agricultural Economics and Rural Sociology, vol. 6(1), pages 177-200.
    15. Marta Rocchi & Guglielmo Pescatore, 2022. "Modeling narrative features in TV series: coding and clustering analysis," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    16. Jonas M. B. Haslbeck & Dirk U. Wulff, 2020. "Estimating the number of clusters via a corrected clustering instability," Computational Statistics, Springer, vol. 35(4), pages 1879-1894, December.
    17. Xiaobei Zhao & Eivind Valen & Brian J Parker & Albin Sandelin, 2011. "Systematic Clustering of Transcription Start Site Landscapes," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-16, August.
    18. Shaza B. Zaghlool & Anna Halama & Nisha Stephan & Valborg Gudmundsdottir & Vilmundur Gudnason & Lori L. Jennings & Manonanthini Thangam & Emma Ahlqvist & Rayaz A. Malik & Omar M. E. Albagha & Abdul Ba, 2022. "Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    19. Nikolaos Karapetsas & Thomas K. Alexandridis & George Bilas & Serafeim Theocharis & Stefanos Koundouras, 2023. "Delineating Natural Terroir Units in Wine Regions Using Geoinformatics," Agriculture, MDPI, vol. 13(3), pages 1-18, March.
    20. Sara Dolnicar & Friedrich Leisch, 2017. "Using segment level stability to select target segments in data-driven market segmentation studies," Marketing Letters, Springer, vol. 28(3), pages 423-436, September.
    21. Joeri Hofmans & Eva Ceulemans & Douglas Steinley & Iven Mechelen, 2015. "On the Added Value of Bootstrap Analysis for K-Means Clustering," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 268-284, July.
    22. Hoop, Daniel & Mack, Gabriele & Mann, Stefan & Schmid, Dierk, 2014. "On the dynamics of agricultural labour input and their impact on productivity and income: an empirical study of Swiss family farms," International Journal of Agricultural Management, Institute of Agricultural Management, vol. 3(4), pages 1-11.
    23. Anastasia Panori, 2017. "A Tale of Hidden Cities," REGION, European Regional Science Association, vol. 4, pages 19-38.
    24. Ana Alina Tudoran, 2022. "A machine learning approach to identifying decision-making styles for managing customer relationships," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 351-374, March.
    25. Wu, Han-Ming, 2011. "On biological validity indices for soft clustering algorithms for gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1969-1979, May.
    26. Alessandro Albano & José Luis García-Lapresta & Antonella Plaia & Mariangela Sciandra, 2023. "A family of distances for preference–approvals," Annals of Operations Research, Springer, vol. 323(1), pages 1-29, April.
    27. Matthew Whitaker & Joshua Elliott & Marc Chadeau-Hyam & Steven Riley & Ara Darzi & Graham Cooke & Helen Ward & Paul Elliott, 2022. "Persistent COVID-19 symptoms in a community study of 606,434 people in England," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    28. Rabea Aschenbruck & Gero Szepannek & Adalbert F. X. Wilhelm, 2023. "Imputation Strategies for Clustering Mixed-Type Data with Missing Values," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 2-24, April.
    29. Gallegos, María Teresa & Ritter, Gunter, 2013. "Strong consistency of k-parameters clustering," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 14-31.
    30. Cabral, Laura & Kim, Amy M., 2020. "An empirical reappraisal of the four types of cyclists," Transportation Research Part A: Policy and Practice, Elsevier, vol. 137(C), pages 206-221.
    31. Liao, Tim F. & Bolano, Danilo & Brzinsky-Fay, Christian & Cornwell, Benjamin & Fasang, Anette Eva & Helske, Satu & Piccarreta, Raffaella & Raab, Marcel & Ritschard, Gilbert & Struffolino, Emanuela & S, 2022. "Sequence analysis: Its past, present, and future," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 107, pages 1-1.
    32. Aurora Torrente & Juan Romo, 2021. "Initializing k-means Clustering by Bootstrap and Data Depth," Journal of Classification, Springer;The Classification Society, vol. 38(2), pages 232-256, July.
    33. Chang, Jinyuan & Hu, Qiao & Kolaczyk, Eric D. & Yao, Qiwei & Yi, Fengting, 2024. "Edge differentially private estimation in the β-model via jittering and method of moments," LSE Research Online Documents on Economics 122099, London School of Economics and Political Science, LSE Library.
    34. Stancu Stelian & Pernici Andreea, 2023. "Assessing the Evolution of the Energy Mix Worldwide, with a Focus on the Renewable Energy Transition," Management & Marketing, Sciendo, vol. 18(s1), pages 384-397, December.

  14. Hennig, Christian & Hausdorf, Bernhard, 2004. "Distance-based parametric bootstrap tests for clustering of species ranges," Computational Statistics & Data Analysis, Elsevier, vol. 45(4), pages 875-895, May.

    Cited by:

    1. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    2. Bel, L. & Allard, D. & Laurent, J.M. & Cheddadi, R. & Bar-Hen, A., 2009. "CART algorithm for spatial data: Application to environmental and ecological data," Computational Statistics & Data Analysis, Elsevier, vol. 53(8), pages 3082-3093, June.

  15. Hennig, Christian, 2003. "Clusters, outliers, and regression: fixed point clusters," Journal of Multivariate Analysis, Elsevier, vol. 86(1), pages 183-212, July.

    Cited by:

    1. Rainer Schlittgen, 2011. "A weighted least-squares approach to clusterwise regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 205-217, June.
    2. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    3. Müller, Christine H. & Garlipp, Tim, 2005. "Simple consistent cluster methods based on redescending M-estimators with an application to edge identification in images," Journal of Multivariate Analysis, Elsevier, vol. 92(2), pages 359-385, February.
    4. L. A. García‐Escudero & A. Gordaliza & R. San Martín & S. Van Aelst & R. Zamar, 2009. "Robust linear clustering," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 301-318, January.
    5. Francesca Torti & Domenico Perrotta & Marco Riani & Andrea Cerioli, 2019. "Assessing trimming methodologies for clustering linear regression 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. 13(1), pages 227-257, March.
    6. García-Escudero, L.A. & Gordaliza, A. & Mayo-Iscar, A. & San Martín, R., 2010. "Robust clusterwise linear regression through trimming," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3057-3069, December.
    7. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
    8. Bai, Xiuqin & Yao, Weixin & Boyer, John E., 2012. "Robust fitting of mixture regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2347-2359.
    9. D'Urso, Pierpaolo & Santoro, Adriana, 2006. "Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 287-313, November.

  16. Hennig, Christian & Christlieb, Norbert, 2002. "Validating visual clusters in large datasets: fixed point clusters of spectral features," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 723-739, October.

    Cited by:

    1. Hennig, Christian & Hausdorf, Bernhard, 2004. "Distance-based parametric bootstrap tests for clustering of species ranges," Computational Statistics & Data Analysis, Elsevier, vol. 45(4), pages 875-895, May.
    2. Hennig, Christian, 2003. "Clusters, outliers, and regression: fixed point clusters," Journal of Multivariate Analysis, Elsevier, vol. 86(1), pages 183-212, July.
    3. L. A. García‐Escudero & A. Gordaliza & R. San Martín & S. Van Aelst & R. Zamar, 2009. "Robust linear clustering," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 301-318, January.
    4. Jose Ameijeiras-Alonso & Jochen Einbeck, 2024. "A fresh look at mean-shift based modal 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. 18(4), pages 1067-1095, December.
    5. Woodruff, David L. & Reiners, Torsten, 2004. "Experiments with, and on, algorithms for maximum likelihood clustering," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 237-253, September.
    6. Garcia-Escudero, L.A. & Gordaliza, A., 2007. "The importance of the scales in heterogeneous robust clustering," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4403-4412, May.

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