PCA-Kernel Estimation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Journal Articles Statistics & Risk Modeling with Applications in Finance and Insurance Year : 2012

PCA-Kernel Estimation


Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample $\bX_1, \hdots, \bX_n$ onto the first $D$ eigenvectors of the Principal Component Analysis (PCA) associated with the empirical projector $\hat \Pi_D$. Classical nonparametric inference methods such as kernel density estimation or kernel regression analysis are then performed in the (usually small) $D$-di\-men\-sio\-nal space. However, the mathematical analysis of this data-driven dimension reduction scheme raises technical problems, due to the fact that the random variables of the projected sample $( \hat \Pi_D\bX_1,\hdots, \hat \Pi_D\bX_n )$ are no more independent. As a reference for further studies, we offer in this paper several results showing the asymptotic equivalencies between important kernel-related quantities based on the empirical projector and its theoretical counterpart. As an illustration, we provide an in-depth analysis of the nonparametric kernel regression case
Fichier principal
Vignette du fichier
KPCAv3.pdf (240.92 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-00467013 , version 1 (25-03-2010)



Gérard Biau, André Mas. PCA-Kernel Estimation. Statistics & Risk Modeling with Applications in Finance and Insurance, 2012, 29 (1), pp.19-46. ⟨10.1524/strm.2012.1084⟩. ⟨hal-00467013⟩
365 View
432 Download



Gmail Facebook X LinkedIn More