Robust functional principal components: A projection-pursuit approach
Juan Lucas Bali, Graciela Boente, David E. Tyler, Jane-Ling Wang
The Annals of Statistics · 2011 · Volume 39 · Issue 6
Abstract
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.
Keywords
62G20, 62G35, 62H25, Fisher-consistency, functional data, Method of sieves, Outliers, Penalization, Principal Component Analysis, robust estimation
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