Consistency and asymptotic normality of wavelet estimator in a nonparametric regression model

被引：4

作者：

Shen, Aiting ^{[1
]}

Li, Xiang ^{[1
]}

Zhang, Yajing ^{[1
]}

Qiu, Yige ^{[1
]}

Wang, Xuejun ^{[1
]}

机构：

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

来源：

STOCHASTICS-AN INTERNATIONAL JOURNAL OF PROBABILITY AND STOCHASTIC PROCESSES | 2021年 / 93卷 / 06期

基金：

中国国家自然科学基金;

关键词：

Nonparametric regression model; wavelet estimator; consistency; asymptotic normality; phi-mixing errors; FIXED-DESIGN REGRESSION; CENTRAL-LIMIT-THEOREM; INVARIANCE-PRINCIPLES; EMPIRICAL PROCESSES; MIXING SEQUENCES; SAMPLE QUANTILES; WEAK-CONVERGENCE; ARRAYS;

D O I：

10.1080/17442508.2020.1815745

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, the nonparametric regression model with repeated measurements based on phi-mixing errors is considered. Therth mean consistency, strong consistency, strong convergence rate, complete consistency and the asymptotic normality of the wavelet estimator are established under some mild conditions on moments and mixing coefficients.

引用

页码：868 / 885

页数：18

共 42 条

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