PCA-Kernel Estimation

Abstract : 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
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Statistics and Risk Modeling, De Gruyter, 2012, pp.29, 19-46
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  • HAL Id : hal-00467013, version 1
  • ARXIV : 1003.5089

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Gérard Biau, André Mas. PCA-Kernel Estimation. Statistics and Risk Modeling, De Gruyter, 2012, pp.29, 19-46. 〈hal-00467013〉

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