Large deviation results for the nonparametric regression function estimator on functional data

被引：1

作者：

Louani D. ^{[1
]}

Ould Maouloud S.M. ^{[2
]}

机构：

[1] Univ. de Reims and L.S.T.A., Univ. de Paris 6, Paris

[2] École des Mines de Mauritanie, Mauritanie

来源：

Mathematical Methods of Statistics | 2012年 / 21卷 / 4期

关键词：

entropy; functional data; kernel estimator; large deviation; regression function; VC-classes;

D O I：

10.3103/S1066530712040035

中图分类号：

学科分类号：

摘要：

This paper is devoted to the study of large deviation behavior in the setting of the estimation of the regression function on functional data. A large deviation principle is stated for a process Zn, defined below, allowing to derive a pointwise large deviation principle for the Nadaraya- Watson-type l-indexed regression function estimator as a by-product. Moreover, a uniform over VC-classes Chernoff type large deviation result is stated for the deviation of the l-indexed regression estimator. © 2012 Allerton Press, Inc.

引用

页码：298 / 313

页数：15

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