Open Access
December 2011 Robust functional principal components: A projection-pursuit approach
Juan Lucas Bali, Graciela Boente, David E. Tyler, Jane-Ling Wang
Ann. Statist. 39(6): 2852-2882 (December 2011). DOI: 10.1214/11-AOS923


In many situations, data are recorded over a period of time and may be regarded as realizations of a stochastic process. In this paper, robust estimators for the principal components are considered by adapting the projection pursuit approach to the functional data setting. Our approach combines robust projection-pursuit with different smoothing methods. Consistency of the estimators are shown under mild assumptions. The performance of the classical and robust procedures are compared in a simulation study under different contamination schemes.


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Juan Lucas Bali. Graciela Boente. David E. Tyler. Jane-Ling Wang. "Robust functional principal components: A projection-pursuit approach." Ann. Statist. 39 (6) 2852 - 2882, December 2011.


Published: December 2011
First available in Project Euclid: 24 January 2012

zbMATH: 1246.62145
MathSciNet: MR3012394
Digital Object Identifier: 10.1214/11-AOS923

Primary: 62G35 , 62H25
Secondary: 62G20

Keywords: Fisher-consistency , functional data , Method of sieves , Outliers , Penalization , Principal Component Analysis , robust estimation

Rights: Copyright © 2011 Institute of Mathematical Statistics

Vol.39 • No. 6 • December 2011
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