CLT for integrated square error of density estimators with censoring indicators missing at random

被引：0
作者：
Yu-Ye Zou
Han-Ying Liang
机构：
[1] Shanghai Maritime University,College of Economics and Management
[2] Tongji University,School of Mathematical Science
来源：
Statistical Papers | 2020年 / 61卷
关键词：
Asymptotic normality; Hellinger distance; Integrated square error; Missing at random; Strong consistency; 62N01; 62G07;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A popular stochastic measure of the distance between the density of the lifetimes and its estimator is the integrated square error (ISE) and Hellinger distance (HD). In this paper, we focus on the right-censored model when the censoring indicators are missing at random. Based on two density estimators defined by Wang et al.(J Multivar Anal 100:835–850, 2009), and another new kernel estimator of the density, we established the asymptotic normality of the ISE and HD for the proposed estimators. In addition, the uniformly strongly consistency of the new kernel estimator of the density is discussed. Also, a simulation study is conducted to compare finite-sample performance of the proposed estimators.
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页码：2685 / 2714
页数：29
相关论文
共 63 条
[1]  
Beran RJ(1977)Minimum Hellinger distance estimates for parametric models Ann Stat 5 445-463
[2]  
Chatrabgoun O(2017)A Legendre multiwavelets approach to copula density estimation Stat Papers 58 673-690
[3]  
Parham G(1982)Nonparametric estimation for partially-complete time and type of failure data Biometrics 38 417-431
[4]  
Chinipardaz R(1984)Central limit theorem for integrated square error of multivariate nonparametric density estimators J Multivar Anal 14 1-16
[5]  
Dinse GE(2012)Nonparametric estimation of the derivatives of a density by the method of wavelet for mixing sequences Stat Papers 53 195-203
[6]  
Hall P(2012)Central limit theorem for ISE of kernel density estimators in censored dependent model Commun Stat Theory Methods 41 1334-1349
[7]  
Hosseinioun N(1981)Regression analysis with randomly right censored data Ann Stat 9 1276-1288
[8]  
Doosti H(2013)Kernel estimation of conditional density with truncated, censored and dependent data J Multivar Anal 120 40-58
[9]  
Nirumand HA(2016)Asymptotic normality of conditional density estimation with left-truncated and dependent data Stat Papers 57 1-20
[10]  
Jomhoori S(2011)Empirical likelihood for density-weighted average derivatives Stat Papers 52 391-412
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