Non-parametric hazard function estimation using the Kaplan-Meier estimator

被引：10
|
作者：
Kim, C [1 ]
Bae, W
Choi, H
Park, BU
机构：
[1] Pusan Natl Univ, Dept Stat, Pusan 609735, South Korea
[2] Inje Univ, Dept Data Sci, Kyungnam 621749, South Korea
[3] Korea Adv Inst Sci & Technol, Div Appl Math, Taejon 305701, South Korea
[4] Seoul Natl Univ, Dept Stat, Seoul 151747, South Korea
来源：
JOURNAL OF NONPARAMETRIC STATISTICS | 2005年 / 17卷 / 08期
关键词：
bandwidth; Kaplan-Meier estimator; kernel smoothing;
D O I：
10.1080/10485250500337138
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：
Estimation of the hazard function when the data are censored is an important problem in medical research. In this article, we propose a simple non-parametric estimator of the hazard function. Its asymptotic properties are derived, and numerical comparisons with other existing estimators are made. The proposed estimator is shown to be at least as good as the other estimators from both the theoretical and the numerical points of view.
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页码：937 / 948
页数：12
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