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Analysis of incomplete data in the presence of dependent competing risks from Marshall–Olkin bivariate Weibull distribution under progressive hybrid censoring
Authors:Jing Cai  Yimin Shi  Bin Liu
Institution:1. Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, China;2. College of Science, Guizhou Minzu University, Guiyang, China
Abstract:This article considers the statistical analysis of dependent competing risks model with incomplete data under Type-I progressive hybrid censored condition using a Marshall–Olkin bivariate Weibull distribution. Based on the expectation maximum algorithm, maximum likelihood estimators for the unknown parameters are obtained, and the missing information principle is used to obtain the observed information matrix. As the maximum likelihood approach may fail when the available information is insufficient, Bayesian approach incorporated with auxiliary variables is developed for estimating the parameters of the model, and Monte Carlo method is employed to construct the highest posterior density credible intervals. The proposed method is illustrated through a numerical example under different progressive censoring schemes and masking probabilities. Finally, a real data set is analyzed for illustrative purposes.
Keywords:Dependent competing risks  Incomplete data  Marshall–Olkin bivariate Weibull distribution  Type-I progressive hybrid censoring
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