Parameters Estimation of the Exponentiated Chen Distribution Based on Upper Record Values

Abstract

This article discusses the Bayesian and frequentist inferences for the exponentiated Chen distribution assuming upper record values. Due to unavailability of the compact form of marginal posterior distributions, a Markov Chain Monte Carlo algorithm is designed to compute the posterior summaries. Prediction of future record values under Bayesian and frequentist methods is also discussed mathematically and numerically. Further, a sensitivity analysis to assess the effect of prior on the estimated parameters is also a part of this study. Besides the simulation studies, the importance of the present study is illustrated with the help of a real data example. It is noted that the Bayes estimates outperform the frequentist inference.