Nonparametric robust regression estimation for censored data

被引：0

作者：

Mohamed Lemdani

Elias Ould Saïd

机构：

[1] Univ. de Lille 2.,Lab. Biomathématiques, Fac. de Pharmacie

[2] Univ. Lille Nord de France,undefined

[3] Univ. du Littoral Côte d’Opale,undefined

[4] LMPA,undefined

来源：

Statistical Papers | 2017年 / 58卷

关键词：

Asymptotic normality; Censored data; Kaplan-Meier estimator; Kernel estimator; Robust estimation; Uniform almost sure convergence; Primary 62G20; Secondary 62G07; 62N01; 62E20;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, we consider a robust regression estimator when the interest random variable is subject to random right-censoring. Based on the so-called synthetic data, we define a new kernel estimator. Under classical conditions and using a VC-classes theory, we establish its uniform consistency with rate and asymptotic normality properties. Special cases are studied and simulations are drawn to illustrate the main results.

引用

页码：505 / 525

页数：20

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