High Dimensional Principal Projections

André Mas (1) , Frits Ruymgaart (2)

1 IMAG - Institut Montpelliérain Alexander Grothendieck
2 Department of Mathematics and Statistics [Texas Tech]

The Principal Component Analysis (PCA) is a famous technique from multivariate statistics. It is frequently carried out in dimension reduction either for functional data or in a high dimensional framework. To that aim PCA yields the eigenvectors $\left( \widehat{\varphi}_{i}\right) _{i}$ of the covariance operator of a sample of interest. Dimension reduction is obtained by projecting on the eigenspaces spanned by the $\widehat{\varphi}% _{i}$'s usually endowed with nice properties in term of optimal information. We focus on the empirical eigenprojectors in the functional PCA of a $n$% -sample and prove several non asymptotic results. More specifically we provide an upper bound for their mean square risk. This rate does not depend on the rate of decrease of the eigenvalues which seems to be a new result. We also derive a lower bound on the risk. The latter matches the upper bound up to a $\log n$ term. The results are applied to improve a technique of nonparametric functional estimation.

Mots clés: Index Terms — Funtional principal Component Analysis; Nonparamet-; Dimension reduction; ric functional regression; Perturbation theory; Covariance operator

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

André Mas, Frits Ruymgaart. High Dimensional Principal Projections. Complex Analysis and Operator Theory, 2015, 9 (1), pp.35 - 63. ⟨10.1007/s11785-014-0371-5⟩. ⟨hal-00772880v2⟩