Estimation in Generalized Lindley distribution with randomly censored data

In this article, we obtained the point and interval estimations for a generalized Lindley distribution (GLD) based on randomly censored data. The maximum likelihood (ML) and Bayes estimation method are used to estimate the unknown parameters of the GLD. Furthermore, approximate confidence intervals (ACIs) for the unknown parameters were constructed. Markov chain Monte Carlo (MCMC) method applied to find the Bayes estimation. Also, highest posterior density (HPD) credible intervals (CRIs) were obtained for the parameters. Gibbs within Metropolis-Hasting samplers used to generate samples from the posterior density functions. A real data set is discussed to illustrate the proposed methods. we performed a Monte Carlo simulation study.