Wavelet estimation in varying coefficient models for censored dependent data

被引：6

作者：

Zhou, Xing-cai ^{[1
,2
]}

Xu, Ying-zhi ^{[1
]}

Lin, Jin-guan ^{[3
]}

机构：

[1] Southeast Univ, Sch Econ & Management, Nanjing, Jiangsu, Peoples R China

[2] Tongling Univ, Sch Math & Comp, Tongling, Peoples R China

[3] Nanjing Audit Univ, Inst Stat & Data Sci, Nanjing, Jiangsu, Peoples R China

来源：

STATISTICS & PROBABILITY LETTERS | 2017年 / 122卷

基金：

中国博士后科学基金;

关键词：

Wavelet estimation; Varying coefficient; Strong mixing; Censored data; PARTIALLY LINEAR-MODELS; ASYMPTOTIC PROPERTIES; REGRESSION-MODELS; TIME-SERIES; DENSITY; ERRORS;

D O I：

10.1016/j.spl.2016.11.009

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 discuss the estimation of varying coefficient models based on censored data by wavelet technique when the survival and the censoring times are from a stationary alpha-mixing sequence. For the wavelet estimator of varying coefficient functions, the strong uniform convergence rate is derived and the asymptotic normality is established under the mild conditions. The strong uniform convergence rate we obtained is comparable with the optimal convergence rate of the nonparametric estimation in nonparametric models. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：179 / 189

页数：11

共 30 条

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