Bayesian estimation for the type-I hybrid xgamma distribution using asymmetric loss function

This article proposes the Bayes estimation of parameter and reliability function for xgamma distribution in presence type-I hybrid censored observations. The estimate has been obtained by assuming informative non-informative priors using general entropy loss function. Obviously, censoring adds difficulties procedure; hence estimators computed with observation under mentioned prior often do not assume any standard form. Therefore, estimates are Tierney-Kadane approximation Markov Chain Monte Carlo numerical technique. Further, different interval namely asymptotic confidence interval, bootstrap highest posterior density along width coverage probability also discussed. maximum likelihood same non- linear maximization iterative procedure compared corresponding simulations. comparison made terms average over whole sample space length interval. lastly, one medical data set considered real application proposed study.