Objective Bayesian inference for the Dhillon distribution

Abstract: In this work, we develop an objective Bayesian framework for the Dhillon probability distribution. We explicitly derive three objective priors: the Jeffreys prior, the overall reference prior, and the maximal data information prior. We show that both Jeffreys and reference priors yields a proper posterior distribution, whereas the maximal data information prior leads to an improper posterior. Moreover, we establish sufficient conditions for the existence of its respective posterior moments. Bayesian inference is carried out via Markov chain Monte Carlo, using the Metropolis-Hastings algorithm. A comprehensive simulation study compares the Bayesian estimators to maximum likelihood estimators in terms of bias, mean squared error, and coverage probability. Finally, a real-data application illustrates the practical utility of the proposed Bayesian approach.