Statistical inference for dependence competing risks model under middle censoring

被引：4
|
作者：
Wang Yan [1 ,2 ]
Shi Yimin [1 ]
Wu Min [3 ]
机构：
[1] Northwestern Polytech Univ, Dept Appl Math, Xian 710072, Shaanxi, Peoples R China
[2] Xian Polytech Univ, Sch Sci, Xian 710048, Shaanxi, Peoples R China
[3] Shanghai Maritime Univ, Sch Econ & Management, Shanghai 201306, Peoples R China
来源：
JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONICS | 2019年 / 30卷 / 01期
基金：
中国国家自然科学基金;
关键词：
middle censoring; dependent competing risks model; Marshall-Olkin bivariate Weibull (MOBW) distribution; acceptance-rejection sampling; EXPONENTIAL-DISTRIBUTION;
D O I：
10.21629/JSEE.2019.01.20
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Middle censoring is an important censoring scheme, in which the actual failure data of an observation becomes un-observable if it falls into a random interval. This paper considers the statistical analysis of the dependent competing risks model by using the Marshall-Olkin bivariate Weibull (MOBW) distribution. The maximum likelihood estimations (MLEs), midpoint approximation (MPA) estimations and approximate confidence intervals (ACIs) of the unknown parameters are obtained. In addition, the Bayes approach is also considered based on the Gamma-Dirichlet prior of the scale parameters, with the given shape parameter. The acceptance-rejection sampling method is used to obtain the Bayes estimations and construct credible intervals (CIs). Finally, two numerical examples are used to show the performance of the proposed methods.
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页码：209 / 222
页数：14
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