Bayesian Estimation of the Entropy of the Half-Logistic Distribution Based on Type-II Censored Samples

Abstract

This paper provides statistical inferences on the entropy of the half-logistic distribution when the data are Type-II censored. The maximum likelihood estimator of the entropy and corresponding approximate confidence interval are derived. For Bayesian inferences, an objective Bayesian estimation method is developed based on the Jeffreys prior. The proposed estimation methods are compared through Monte Carlo simulations for various Type-II censoring schemes. Finally, real data are analyzed for illustration purposes.