Parameter Estimation of Power Lindley Distribution under Hybrid Censoring

Abstract

The present study deals with the classical and Bayesian estimation of the hybrid censored lifetime data under the assumption that the lifetimes follow the power Lindley distribution. By assuming Jeffrey’s invariant and gamma priors of the unknown parameters, Bayes estimates along with posterior standard error and highest posterior density credible intervals of the parameters are obtained. A Markov Chain Monte Carlo technique such as Metropolis-Hastings algorithm has been utilized to generate draws from the posterior density of the parameters. A real data set has been analyzed for illustration purpos