Parametric inferences using dependent competing risks data with partially observed failure causes from MOBK distribution under unified hybrid censoring

• Published in: Journal of statistical computation and simulation Vol. 94; no. 2; pp. 376 - 399
• Main Authors: Dutta, Subhankar, Lio, Yuhlong, Kayal, Suchandan
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 22.01.2024; Taylor & Francis Ltd
• Subjects: Asymptotic properties; Bayesian estimation; Bivariate analysis; Confidence intervals; dependent competing risks; Dirichlet problem; Failure times; Fisher information; gamma-Dirichlet prior; HPD credible interval; Markov chains; Maximum likelihood estimators; MOBK distribution; Monte Carlo simulation; Normality; Statistical analysis; unified hybrid censoring
• ISSN: 0094-9655; 1563-5163
• DOI: 10.1080/00949655.2023.2249165

In this communication, various statistical inferential procedures for estimating unknown model parameters are investigated via utilizing partially observed dependent competing risks data under the unified hybrid censoring scheme when the latent failure times follow Marshall-Olkin bivariate Kumaraswamy distribution. The existence and uniqueness of the maximum likelihood estimators (MLEs) have been established. By using asymptotic normality property of MLE, the approximate confidence intervals have been constructed via observed Fisher information matrix. Moreover, Bayes estimates and the highest posterior density credible intervals have been computed under a highly flexible gamma-Dirichlet prior distribution by using Markov chain Monte Carlo technique. In addition, to compare the performance of proposed methods, a Monte Carlo simulation has been carried out. Finally, a real-life data set has been analysed to illustrate the operability and applicability of the methods considered.