Asymptotic normality of wavelet density estimator under censored dependent observations

被引：0

作者：

Niu, Si-li ^{[1
]}

机构：

[1] Tongji Univ, Dept Math, Shanghai 200092, Peoples R China

来源：

ACTA MATHEMATICAE APPLICATAE SINICA-ENGLISH SERIES | 2012年 / 28卷 / 04期

基金：

中国国家自然科学基金;

关键词：

Wavelet density estimator; asymptotic normality; censored data; alpha-mixing; random weighted estimator; HAZARD RATE ESTIMATION; ERROR; RATES;

D O I：

10.1007/s10255-012-0188-9

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we discuss the asymptotic normality of the wavelet estimator of the density function based on censored data, when the survival and the censoring times form a stationary alpha-mixing sequence. To simulate the distribution of estimator such that it is easy to perform statistical inference for the density function, a random weighted estimator of the density function is also constructed and investigated. Finite sample behavior of the estimator is investigated via simulations too.

引用

页码：781 / 794

页数：14

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