Uniform-in-bandwidth kernel estimation for censored data

被引：3
|
作者：
Ouadah, Sarah [1 ]
机构：
[1] Univ Paris 06, LSTA, F-75252 Paris, France
来源：
JOURNAL OF STATISTICAL PLANNING AND INFERENCE | 2013年 / 143卷 / 08期
关键词：
Functional limit laws; Right random censorship model; Kernel lifetime density estimators; Kernel failure rate estimators; Kaplan-Meier empirical process; Convergence in probability; NONPARAMETRIC-ESTIMATION; DENSITY ESTIMATORS; ITERATED LOGARITHM; HAZARD FUNCTION; LIMIT LAWS; CONSISTENCY; INCREMENTS;
D O I：
10.1016/j.jspi.2013.03.017
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：
We present a sharp uniform-in-bandwidth functional limit law for the increments of the Kaplan-Meier empirical process based upon right-censored random data. We apply this result to obtain limit laws for nonparametric kernel estimators of local functionals of lifetime densities, which are uniform with respect to the choices of bandwidth and kernel. These are established in the framework of convergence in probability, and we allow the bandwidth to vary within the complete range for which the estimators are consistent. We provide explicit values for the asymptotic limiting constant for the sup-norm of the estimation random error. (C) 2013 Elsevier B.V. All rights reserved.
引用
收藏
页码：1273 / 1284
页数：12
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