Asymptotic normality for the estimator of non parametric regression model under φ-mixing errors

被引：2

作者：

Zheng, Lulu ^{[1
]}

Ding, Yang ^{[1
]}

Wang, Xuejun ^{[1
]}

机构：

[1] Anhui Univ, Sch Math Sci, Hefei 230601, Peoples R China

来源：

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2017年 / 46卷 / 14期

基金：

中国国家自然科学基金;

关键词：

Asymptotic normality; Non parametric regression model; phi-Mixing random variables; 62G20; FIXED-DESIGN REGRESSION; NONPARAMETRIC MULTIPLE-REGRESSION; LINEAR-TIME SERIES; RANDOM-VARIABLES; COMPLETE CONVERGENCE; SEQUENCES; ARRAYS; SUMS;

D O I：

10.1080/03610926.2015.1134574

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：

In this article, by using the Rosenthal-type inequality and the Bernstein's big-block and small-block procedure, we establish the asymptotic normality for the estimators of non parametric regression model based on phi-mixing errors. The result obtained in the article generalizes some corresponding ones for some dependent random variables.

引用

页码：6764 / 6773

页数：10

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