Bayes Estimation of Lindley Distribution Under Entropy Loss Functions and Type II Censoring

Abstract

This study presents Bayesian estimation for the parameters of the Lindley distribution under Type II censoring, employing entropy loss functions and conjugate priors. Markov Chain Monte Carlo (MCMC) methods are used to derive the posterior distributions, facilitating parameter estimation. The use of conjugate priors streamlines the computational process, enhancing the practicality of the Bayesian framework. Simulation results confirm the effectiveness of the Bayesian estimators under entropy loss, particularly in censored data scenarios. The methodology offers a valuable approach for researchers and practitioners working with reliability and lifetime data, demonstrating the efficiency and accuracy of the proposed Bayesian methods.