On the rates of asymptotic normality for recursive kernel density estimators under φ-mixing assumptions

被引：6

作者：

Xi, Mengmei ^{[1
]}

Wang, Xuejun ^{[1
]}

机构：

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

来源：

JOURNAL OF NONPARAMETRIC STATISTICS | 2019年 / 31卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Berry-Esseen bounds; asymptotic normality; density function; recursive kernel estimator; BERRY-ESSEEN BOUNDS; CONVERGENCE;

D O I：

10.1080/10485252.2019.1566542

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 paper, we mainly consider two kinds of recursive kernel estimators of , which is the probability density function of a sequence of phi-mixing random variables . Under some suitable conditions, we establish the convergence rates of asymptotic normality for the two recursive kernel estimators and . In particular, by the choice of the bandwidths, the convergence rates of asymptotic normality for the estimators and can attain and respectively. Besides, the simulation study and a real data analysis are presented to verify the validity of the theoretical results.

引用

页码：340 / 363

页数：24

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