Bayesian Analysis of Generalized Inverted Exponential Distribution Based on Generalized Progressive Hybrid Censoring Competing Risks Data

Abstract

In this study, a competing risk model was developed under a generalized progressive hybrid censoring scheme using a generalized inverted exponential distribution. The latent causes of failure were presumed to be independent. Estimating the unknown parameters is performed using maximum likelihood (ML) and Bayesian methods. Using the Markov chain Monte Carlo technique, Bayesian estimators were obtained under gamma priors with various loss functions. ML estimate was used to create confidence intervals (CIs). In addition, we present two bootstrap CIs for the unknown parameters. Further, credible CIs and the highest posterior density intervals were constructed based on the conditional posterior distribution. Monte Carlo simulation is used to examine the performance of different estimates. Applications to real data were used to check the estimates and compare the proposed model with alternative distributions.