Sequential estimation for the semiparametric additive hazard model

被引：0
|
作者：
Bordes, L. [1 ]
Breuils, C. [1 ]
机构：
[1] Univ Technol Compiegne, LMAC, F-60206 Compiegne, France
来源：
ADVANCES IN STATISTICAL METHODS FOR THE HEALTH SCIENCES: APPLICATIONS TO CANCER AND AIDS STUDIES, GENOME SEQUENCE ANALYSIS, AND SURVIVAL ANALYSIS | 2007年
关键词：
semiparametric additive hazard model; sequential estimation; right-censored survival data; complete convergence;
D O I：
10.1007/978-0-8176-4542-7_14
中图分类号：
R1 [预防医学、卫生学];
学科分类号：
1004 ; 120402 ;
摘要：
In this chapter, we investigate the asymptotic behavior of the sequential version of the regression parameter estimator for the additive hazard model. We mainly establish that the Lin and Ying (1994) nonsequential estimator is strongly consistent (in the sense of complete convergence) and that this estimator, indexed by any regular sequence (sequential estimator), has the same asymptotic behavior as the nonsequential estimator. An example of a fixed-width confidence-type sequential estimator is illustrated by simulations.
引用
收藏
页码：211 / +
页数：3
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