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.
"Robust functional principal components: A projection-pursuit approach." Ann. Statist. 39 (6) 2852 - 2882, December 2011. https://doi.org/10.1214/11-AOS923