IDEAS home Printed from https://ideas.repec.org/r/eee/csdana/v52y2008i3p1694-1711.html
   My bibliography  Save this item

Outlier identification in high dimensions

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Junlong Zhao & Chao Liu & Lu Niu & Chenlei Leng, 2019. "Multiple influential point detection in high dimensional regression spaces," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 385-408, April.
  2. David E. Tyler & Frank Critchley & Lutz Dümbgen & Hannu Oja, 2009. "Invariant co‐ordinate selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(3), pages 549-592, June.
  3. Bernard, Carole & Vanduffel, Steven, 2015. "A new approach to assessing model risk in high dimensions," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 166-178.
  4. Robert Serfling & Satyaki Mazumder, 2013. "Computationally easy outlier detection via projection pursuit with finitely many directions," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(2), pages 447-461, June.
  5. Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
  6. 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.
  7. 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.
  8. Ayanendranath Basu & Abhik Ghosh & Maria Jaenada & Leandro Pardo, 2024. "Robust adaptive LASSO in high-dimensional logistic regression," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(5), pages 1217-1249, November.
  9. Alexander A. Aduenko & Anastasia P. Motrenko & Vadim V. Strijov, 2018. "Object selection in credit scoring using covariance matrix of parameters estimations," Annals of Operations Research, Springer, vol. 260(1), pages 3-21, January.
  10. 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.
  11. Agnieszka Wnęk & Dawid Kudas & Premysl Stych, 2021. "National Level Land-Use Changes in Functional Urban Areas in Poland, Slovakia, and Czechia," Land, MDPI, vol. 10(1), pages 1-16, January.
  12. Jack Jewson & David Rossell, 2022. "General Bayesian loss function selection and the use of improper models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1640-1665, November.
  13. Shieh Albert D & Hung Yeung Sam, 2009. "Detecting Outlier Samples in Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-26, February.
  14. Heewon Park & Teppei Shimamura & Satoru Miyano & Seiya Imoto, 2014. "Robust Prediction of Anti-Cancer Drug Sensitivity and Sensitivity-Specific Biomarker," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-10, October.
  15. Cerioli, Andrea & Farcomeni, Alessio & Riani, Marco, 2013. "Robust distances for outlier-free goodness-of-fit testing," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 29-45.
  16. Francesca De Battisti & Silvia Salini, 2013. "Robust analysis of bibliometric data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 269-283, June.
  17. Jan Kalina & Jan Tichavský, 2022. "The minimum weighted covariance determinant estimator 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. 16(4), pages 977-999, December.
  18. Silvia Salini & Andrea Cerioli & Fabrizio Laurini & Marco Riani, 2016. "Reliable Robust Regression Diagnostics," International Statistical Review, International Statistical Institute, vol. 84(1), pages 99-127, April.
  19. Valentin Todorov & Matthias Templ & Peter Filzmoser, 2011. "Detection of multivariate outliers in business survey data with incomplete information," 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. 5(1), pages 37-56, April.
  20. Alam, Md. Ashad & Calhoun, Vince D. & Wang, Yu-Ping, 2018. "Identifying outliers using multiple kernel canonical correlation analysis with application to imaging genetics," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 70-85.
  21. Luis Alfonso Menéndez-García & Paulino José García-Nieto & Esperanza García-Gonzalo & Fernando Sánchez Lasheras & Laura Álvarez-de-Prado & Antonio Bernardo-Sánchez, 2023. "Method for the Detection of Functional Outliers Applied to Quality Monitoring Samples in the Vicinity of El Musel Seaport in the Metropolitan Area of Gijón (Northern Spain)," Mathematics, MDPI, vol. 11(12), pages 1-23, June.
  22. Chung, Hee Cheol & Ahn, Jeongyoun, 2021. "Subspace rotations for high-dimensional outlier detection," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
  23. Cerioli, Andrea & Farcomeni, Alessio, 2011. "Error rates for multivariate outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 544-553, January.
  24. G. Zioutas & C. Chatzinakos & T. D. Nguyen & L. Pitsoulis, 2017. "Optimization techniques for multivariate least trimmed absolute deviation estimation," Journal of Combinatorial Optimization, Springer, vol. 34(3), pages 781-797, October.
  25. Gottard, Anna & Pacillo, Simona, 2010. "Robust concentration graph model selection," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3070-3079, December.
  26. Thomas Ortner & Peter Filzmoser & Maia Rohm & Sarka Brodinova & Christian Breiteneder, 2021. "Local projections for high-dimensional outlier detection," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 189-206, August.
  27. Stefano Marchetti & Caterina Giusti & Nicola Salvati & Monica Pratesi, 2017. "Small area estimation based on M-quantile models in presence of outliers in auxiliary variables," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(4), pages 531-555, November.
  28. Montes, Francisco & Sala, Ramón, 2012. "Equilibrio competitivo en Liga española de futbol de Primera División: Un test de Montecarlo basado en datos funcionales/Competitive Balance in the First Division Spanish Soccer League: A Montecarlo T," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 30, pages 513-526, Agosto.
  29. Thomas P. Triebs & Subal C. Kumbhakar, 2018. "Management in production: from unobserved to observed," Journal of Productivity Analysis, Springer, vol. 49(2), pages 111-121, June.
  30. D. Rosadi & P. Filzmoser, 2019. "Robust second-order least-squares estimation for regression models with autoregressive errors," Statistical Papers, Springer, vol. 60(1), pages 105-122, February.
  31. C. Chatzinakos & L. Pitsoulis & G. Zioutas, 2016. "Optimization techniques for robust multivariate location and scatter estimation," Journal of Combinatorial Optimization, Springer, vol. 31(4), pages 1443-1460, May.
  32. Andreas Alfons & Wolfgang Baaske & Peter Filzmoser & Wolfgang Mader & Roland Wieser, 2011. "Robust variable selection with application to quality of life research," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(1), pages 65-82, March.
  33. 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.
  34. Asuman Turkmen & Nedret Billor, 2013. "Partial least squares classification for high dimensional data using the PCOUT algorithm," Computational Statistics, Springer, vol. 28(2), pages 771-788, April.
  35. Francesco Vidoli & Fabio Quintiliani & Giorgio Ivaldi & Giorgia Marinuzzi & Francesco Porcelli & Walter Tortorella, 2024. "Do municipal unions improve cost efficiency for the social function? A quasi‐experimental endogenous stochastic frontier approach," Journal of Regional Science, Wiley Blackwell, vol. 64(2), pages 308-332, March.
  36. Van Aelst, S. & Vandervieren, E. & Willems, G., 2012. "A Stahel–Donoho estimator based on huberized outlyingness," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 531-542.
  37. 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.
  38. Francesca DE BATTISTI & Silvia SALINI, 2011. "Robust analysis of bibliometric data," Departmental Working Papers 2011-36, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
  39. Thomas Triebs & Subal C. Kumbhakar, 2012. "Management Practice in Production," ifo Working Paper Series 129, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.