Nonparametric estimators of the bivariate survival function under simplified censoring conditions

被引：34
|
作者：
Wang, WJ [1 ]
Wells, MT
机构：
[1] Acad Sinica, Inst Stat, Taipei, Taiwan
[2] Cornell Univ, Dept Social Stat, Ithaca, NY 14853 USA
[3] Cornell Univ, Ctr Stat, Ithaca, NY 14853 USA
来源：
BIOMETRIKA | 1997年 / 84卷 / 04期
基金：
美国国家科学基金会; 美国国家卫生研究院;
关键词：
archimedean copula; bivariate failure time data; independent censoring; marginal modelling; univariate censoring;
D O I：
10.1093/biomet/84.4.863
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
New bivariate survival function estimators are proposed in the case where the dependence relationship between the censoring variables are modelled. Specific examples include the cases when censoring variables are univariate, mutually independent or specified by a marginal model. Large sample properties of the proposed estimators are discussed. The finite sample performance of the proposed estimators compared with other fully nonparametric estimators is studied via simulations. A real data example is given.
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页码：863 / 880
页数：18
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