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

AbstractIn 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 distributiondependent competing risksunified hybrid censoringBayesian estimationgamma-Dirichlet priorHPD credible intervalMathematics Subject Classifications 2010: 62F1062F1562H12 AcknowledgmentsThe authors would like thank to the editor, associate editor and anonymous referees for their suggestions and comments, which significantly improved this manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe 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.