Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression

被引：4
|
作者：
Geenens, Gery [1 ]
机构：
[1] UNSW Australia, Sch Math & Stat, Sydney, NSW, Australia
来源：
STATISTICS & PROBABILITY LETTERS | 2015年 / 107卷
关键词：
Nonparametric regression; Functional regression; Scalar-on-function regression; Nadaraya-Watson estimator; Semi-metric; GENERALIZED LINEAR-MODELS;
D O I：
10.1016/j.spl.2015.09.015
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Recently, some nonparametric regression ideas have been extended to the functional context, allowing infinite-dimensional regressors. This paper gives a deep asymptotic study of the functional Nadaraya-Watson estimator, including moments of all orders, errors, asymptotic distribution and large deviation rate. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：369 / 377
页数：9
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