Least-square estimation for regression on random designs for absolutely regular observations

被引：0

作者：

Viennet, G ^{[1
]}

机构：

[1] Univ Paris 07, UFR Math, Dept Math, F-75251 Paris 5, France

来源：

STATISTICS & PROBABILITY LETTERS | 1999年 / 43卷 / 01期

关键词：

absolute regularity; least-square regression; minimum contrast estimation; sieves; non-parametric estimation; strictly stationary sequences;

D O I：

10.1016/S0167-7152(98)00221-1

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：

We investigate the rates of convergence of least-square estimator on sieves for regression under various weak dependency assumptions. We provide a control in probability for the rate of convergence and we show how to construct estimators of a smooth regression function at a rate which is known to be optimal in the independent and identically distributed case. (C) 1999 Published by Elsevier Science B.V. All rights reserved.

引用

页码：13 / 23

页数：11

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