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

Abstract

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.

Keywords:

• MOBK distribution
• dependent competing risks
• unified hybrid censoring
• Bayesian estimation
• gamma-Dirichlet prior
• HPD credible interval

Mathematics Subject Classifications 2010:

• 62F10
• 62F15
• 62H12

Acknowledgments

The authors would like thank to the editor, associate editor and anonymous referees for their suggestions and comments, which significantly improved this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author S. Dutta, thanks the Council of Scientific and Industrial Research (C.S.I.R. Grant No. 09/983(0038)/2019-EMR-I), India, for the financial assistantship, received to carry out this research work. The first and third authors thank the Department of Mathematics, National Institute of Technology Rourkela, India for the available research facilities.