Robust inference for functional data

BOENTE, GRACIELA

Cordoba

Congreso; X CLATSE, Congreso Latinoamericano de Sociedades de Estadistica; 2012

Univ. Nacional de Cordoba, Sociedad Argentina de Estadistica, Grupo Argentino de Biometria, Soche, Soc. Estadistica Uruguay

When dealing with just one population, classical PCA, searches for directions with maximal dispersion of the data projected on it. Instead of using the variance as a measure of dispersion, a robust scale estimator may be used in the maximization problem, in order to obtain more resistant procedures. During this talk, we review some of the proposed approaches to robust functional PCA including one which adapts the projection pursuit approach to the functional data setting. Besides, we will discuss an extension that provides robust approach to estimate the common directions and their size for robust functional principal component analysis.