Strong uniform consistency of a nonparametric estimator of a conditional quantile for censored dependent data and functional regressors

被引：13

作者：

Horrigue, Walid ^{[1
,2
]}

Said, Elias Ould ^{[1
,2
]}

机构：

[1] Univ Lille Nord France, F-59000 Lille, France

[2] ULCO, LMPA, F-62228 Calais, France

来源：

RANDOM OPERATORS AND STOCHASTIC EQUATIONS | 2011年 / 19卷 / 02期

关键词：

Censored data; conditional distribution function; infinite dimension; Kaplan-Meier; estimator; kernel estimator; small-ball probability;

D O I：

10.1515/ROSE.2011.008

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：

Let. (T, C, X) be a vector of random variables (rvs) where T, C and X are the interest variable, a right censoring rv and a covariate, respectively. In this paper, we study the kernel conditional quantile estimation in the dependent case and when the covariable takes values in an infinite-dimension space. An estimator of the conditional quantile is given and, under some regularity conditions, among which the small-ball probability for the covariate, its uniform strong convergence with rates is established.

引用

页码：131 / 156

页数：26

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