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Reliability Estimation for Inverse Lomax Distribution Under Type-II Censored Data Using Markov Chain Monte Carlo Method

S. K. Singh, Umesh Singh, Abhimanyu Singh Yadav


In this study, we consider the classical and Bayesian estimation for the parameters, reliability and hazard functions of Inverse Lomax distribution (ILD) under Type-II censoring scheme. The classical estimates i.e. maximum likelihood estimates (MLEs) are obtained by
using non-linear maximization technique. The Bayes estimates are obtained under squared error loss function (SELF) through Markov Chain Monte Carlo Method. We have also computed the 95% asymptotic confidence intervals and highest posterior density (HPD) intervals for the parameters. The comparison of the Bayes estimators with corresponding ML estimators has been done through Monte Carlo simulations. Finally, a real life application is provided to illustrate the study in realistic phenomenon.


Inverse Lomax Model, Classical and Bayesian estimation, Markov Chain Monte Carlo Method.

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