Parameter Estimation For Inverse Pareto Distribution With Randomly Censored Life Time Data

This article deals with the classical and Bayesian estimation procedures of the parameters of inverse Pareto distribution (IPD) using randomly censored data. The Maximum likelihood and Bayes estimators of the parameters are derived. The asymptotic confidence intervals of the parameters based on observed Fisher information matrix are constructed. Bayes estimators of the parameters are derived under LINEX loss function using gamma informative priors. For Bayesian computations, Markov Chain Monte Carlo (MCMC) method is used. Also, highest posterior density credible intervals of the parameters based on MCMC techniques are constructed. A simulation study compares the performance of the various estimators. Finally, two real datasets are considered for illustrative purposes.