Inference of progressively censored competing risks data from Kumaraswamy distributions

被引：27
|
作者：
Wang, Liang [1 ]
机构：
[1] Xidian Univ, Sch Math & Stat, 2 Taiba Nanlu, Xian 710071, Shaanxi, Peoples R China
来源：
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2018年 / 343卷
基金：
中国国家自然科学基金;
关键词：
Kumaraswamy distribution; Competing risks; Progressive censoring; Maximum likelihood estimation; Bayesian estimation; Monte-Carlo simulation; PROBABILITY; FAILURE; SAMPLES;
D O I：
10.1016/j.cam.2018.05.013
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A competing risks model based on Kumaraswamy distribution is discussed under progressive censoring. When the latent lifetime model of failure causes features different and common parameters, maximum likelihood estimates for unknown parameters are established where the existence and uniqueness of the estimates are provided, and the approximate confidence intervals are also constructed via the observed fisher information matrix. Moreover, Bayes estimates and associated highest posterior density credible intervals are also obtained based on Monte-Carlo Markov chain sampling methods. In addition, to test the equivalence of parameters between the competing risks, likelihood ratio test is also proposed. Finally, simulation studies and real-life example are presented for illustration purpose. (C) 2018 Elsevier B.V. All rights reserved.
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页码：719 / 736
页数：18
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