Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution

In this article,we study the geometric distribution under randomly censored data. maximum likelihood estimators and confidence intervals based on fisher information matrix are derived for the unknown parameters with randomly censored data. bayes estimators are also developed using beta priors under generalized entropy and linex loss functions. also,bayesian credible and highest posterior density (hpd) credible intervals are obtained for the parameters. expected time on test and reliability characteristics are also analyzed in this article. to compare various estimates developed in the article,a monte carlo simulation study is carried out. finally,for illustration purpose,a randomly censored real data set is discussed. © 2017 hare krishna and neha goel.