MINIMALLY BIASED NONPARAMETRIC REGRESSION AND AUTOREGRESSION

被引：0

作者：

McMurry, Timothy L. ^{[1
]}

Politis, Dimitris N. ^{[2
]}

机构：

[1] Depaul Univ, Dept Math Sci, Chicago, IL 60604 USA

[2] Univ Calif San Diego, Dept Math, La Jolla, CA 92093 USA

来源：

REVSTAT-STATISTICAL JOURNAL | 2008年 / 6卷 / 02期

关键词：

nonparametric regression; autoregression; Fourier transform;

D O I：

暂无

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：

A nonparametric regression estimator is introduced which adapts to the smoothness of the unknown function being estimated. This property allows the new estimator to automatically achieve minimal bias over a large class of locally smooth functions without changing the rate at which the variance converges. Optimal convergence rates are shown to hold for both i.i.d. data and autoregressive processes satisfying strong mixing conditions.

引用

页码：123 / +

页数：27

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