Robust functional principal components: A projection-pursuit approach

By Juan Lucas Bali, Graciela Boente, David E. Tyler, Jane-ling Wang. 2011

100 citations recorded by the source corpus.

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.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/381897b0be15eb43805f105552d5cbf26f90ceea

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In Statistics and Probability

Citations in the corpus

Functional Data Analysis (2016, methodology): “…and contamination and more generally visual exploration of functional data include exploratory box plots (Hyndman & Shang 2010; Sun & Genton 2011) and robust versions of FPCA (Crambes, Delsol & Laksaci 2008; Gervini 2008; Bali et al. 2011; Kraus & Panaretos 2012; Boente & Salibi´an-Barrera 2014).”
A survey of functional principal component analysis (2014, methodology): “The asymptotic properties of a robust projection - pursuit approach were studied by Bali et al .”
Dispersion operators and resistant second-order functional data analysis (2012, methodology): “Bali et al. (2012) adapted the projection-pursuit method of Li & Chen (1985) in the functional case.”