Bayesian analysis of Weibull distribution based on progressive type-II censored competing risks data with binomial removals

• Published in: Computational statistics Vol. 34; no. 1; pp. 233 - 252
• Main Authors: Chacko, Manoj, Mohan, Rakhi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2019; Springer Nature B.V
• Subjects: Bayesian analysis; Binomial distribution; Economic models; Economic Theory/Quantitative Economics/Mathematical Methods; Estimators; Failure analysis; Mathematics and Statistics; Original Paper; Probability and Statistics in Computer Science; Probability Theory and Stochastic Processes; Reliability analysis; Risk analysis; Statistics; Weibull distribution; Competing risks; Bayes estimates; Progressive type-II censoring; MCMC method
• ISSN: 0943-4062; 1613-9658
• DOI: 10.1007/s00180-018-0847-2

In medical studies or reliability analysis, the failure of individuals or items may be due to more than one cause or factor. These risk factors in some sense compete for the failure of the experimental units. Analysis of data in this circumstances is called competing risks analysis. In this paper, we consider the analysis of competing risk data under progressive type-II censoring by assuming the number of units removed at each stage is random and follows a binomial distribution. Bayes estimators are obtained by assuming the population under consider follows a Weibull distribution. A simulation study is carried out to study the performance of the different estimators derived in this paper. A real data set is also used for illustration.