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High breakdown estimators for principal components: the projection-pursuit approach revisited

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  1. Heinrich Fritz & Peter Filzmoser & Christophe Croux, 2012. "A comparison of algorithms for the multivariate L 1 -median," Computational Statistics, Springer, vol. 27(3), pages 393-410, September.
  2. Kondylis, Athanassios & Hadi, Ali S., 2006. "Derived components regression using the BACON algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 556-569, November.
  3. Brooks, J.P. & Dulá, J.H. & Boone, E.L., 2013. "A pure L1-norm principal component analysis," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 83-98.
  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. Bali, Juan Lucas & Boente, Graciela, 2015. "Influence function of projection-pursuit principal components for functional data," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 173-199.
  6. Lee, Seokho & Shin, Hyejin & Billor, Nedret, 2013. "M-type smoothing spline estimators for principal functions," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 89-100.
  7. Hervé Cardot & Antoine Godichon-Baggioni, 2017. "Fast estimation of the median covariation matrix with application to online robust principal components analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(3), pages 461-480, September.
  8. Marc Hallin & Davy Paindaveine & Thomas Verdebout, 2014. "Efficient R-Estimation of Principal and Common Principal Components," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1071-1083, September.
  9. Bali, Juan Lucas & Boente, Graciela, 2014. "Consistency of a numerical approximation to the first principal component projection pursuit estimator," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 181-191.
  10. B. Barış Alkan, 2016. "Robust Principal Component Analysis Based on Modified Minimum Covariance Determinant in the Presence of Outliers," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 85-94, September.
  11. Serneels, Sven & Verdonck, Tim, 2009. "Principal component regression for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 53(11), pages 3855-3863, September.
  12. 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.
  13. Boente, Graciela & Molina, Julieta & Sued, Mariela, 2010. "On the asymptotic behavior of general projection-pursuit estimators under the common principal components model," Statistics & Probability Letters, Elsevier, vol. 80(3-4), pages 228-235, February.
  14. Luca Greco & Alessio Farcomeni, 2016. "A plug-in approach to sparse and robust principal component analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(3), pages 449-481, September.
  15. Ulrike Genschel, 2018. "The Effect of Data Contamination in Sliced Inverse Regression and Finite Sample Breakdown Point," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 80(1), pages 28-58, February.
  16. Pires, Ana M. & Branco, João A., 2010. "Projection-pursuit approach to robust linear discriminant analysis," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2464-2485, November.
  17. Graciela Boente & Matías Salibian-Barrera, 2015. "S -Estimators for Functional Principal Component Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(511), pages 1100-1111, September.
  18. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
  19. Hong, David & Balzano, Laura & Fessler, Jeffrey A., 2018. "Asymptotic performance of PCA for high-dimensional heteroscedastic data," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 435-452.
  20. Graciela Boente & Frank Critchley & Liliana Orellana, 2007. "Influence functions of two families of robust estimators under proportional scatter matrices," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 295-327, February.
  21. 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.
  22. Cevallos-Valdiviezo, Holger & Van Aelst, Stefan, 2019. "Fast computation of robust subspace estimators," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 171-185.
  23. Sven Serneels, 2019. "Projection pursuit based generalized betas accounting for higher order co-moment effects in financial market analysis," Papers 1908.00141, arXiv.org.
  24. Choulakian, V. & Allard, J. & Almhana, J., 2006. "Robust centroid method," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 737-746, November.
  25. Bali, Juan Lucas & Boente, Graciela, 2017. "Robust estimators under a functional common principal components model," Computational Statistics & Data Analysis, Elsevier, vol. 113(C), pages 424-440.
  26. Mani Bayani, 2021. "Robust PCA Synthetic Control," Papers 2108.12542, arXiv.org, revised Oct 2021.
  27. Stephan Morgenthaler, 2007. "A survey of robust statistics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(3), pages 271-293, February.
  28. 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.
  29. 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.
  30. Kalogridis, Ioannis & Van Aelst, Stefan, 2019. "Robust functional regression based on principal components," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 393-415.
  31. 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.
  32. Jiménez Recaredo, Raúl José & Elías Fernández, Antonio, 2017. "Prediction Bands for Functional Data Based on Depth Measures," DES - Working Papers. Statistics and Econometrics. WS 24606, Universidad Carlos III de Madrid. Departamento de Estadística.
  33. 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.
  34. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
  35. Claudio Agostinelli & Ana M. Bianco & Graciela Boente, 2020. "Robust estimation in single-index models when the errors have a unimodal density with unknown nuisance parameter," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 855-893, June.
  36. Serneels, Sven & Verdonck, Tim, 2008. "Principal component analysis for data containing outliers and missing elements," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1712-1727, January.
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