Nonlinear wavelet density estimation with data missing at random when covariates are present

被引：5

作者：

Zou, Yu-Ye ^{[1
]}

Liang, Han-Ying ^{[1
]}

Zhang, Jing-Jing ^{[2
]}

机构：

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

[2] Shanghai Univ Sci & Technol, Coll Sci, Shanghai 200093, Peoples R China

来源：

METRIKA | 2015年 / 78卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Nonlinear wavelet density estimator; Mean integrated squared error; Missing data; Asymptotic normality; VARYING COEFFICIENT MODELS; EMPIRICAL LIKELIHOOD; RESPONSE DATA; SHRINKAGE; INFERENCE;

D O I：

10.1007/s00184-015-0536-x

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 construct the nonlinear wavelet estimator of a density with data missing at random when covariables are present, and provide an asymptotic expression for the mean integrated squared error (MISE) of the estimator. Unlike for kernel estimators, the MISE expression of the wavelet-based estimator still holds when the density function is piecewise smooth. Also, the asymptotic normality of the estimator is established.

引用

页码：967 / 995

页数：29

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