Objective Bayesian Analysis for the Lomax Distribution

TL;DR

This paper develops Bayesian methods using non-informative priors for estimating Lomax distribution parameters, evaluating their performance through simulations and real data application.

Contribution

It introduces Bayesian inference procedures for the Lomax distribution with Jeffreys and reference priors, assessing their effectiveness.

Findings

Bayesian estimators show low bias and MSE in simulations

Performance varies with different priors, affecting estimation accuracy

Application to real data demonstrates practical utility

Abstract

In this paper we propose to make Bayesian inferences for the parameters of the Lomax distribution using non-informative priors, namely the Jeffreys prior and the reference prior. We assess Bayesian estimation through a Monte Carlo study with 500 simulated data sets. To evaluate the possible impact of prior specification on estimation, two criteria were considered: the bias and square root of the mean square error. The developed procedures are illustrated on a real data set.