Bayesian Estimation of a New Pareto-Type Distribution Based on Mixed Gibbs Sampling Algorithm

Abstract

In this paper, based on the mixed Gibbs sampling algorithm, a Bayesian estimation procedure is proposed for a new Pareto-type distribution in the case of complete and type II censored samples. Simulation studies show that the proposed method is consistently superior to the maximize likelihood estimation in the context of small samples. Also, an analysis of some real data is provided to test the Bayesian estimation.

Keywords

• new Pareto-type distribution
• Bayesian estimation
• the mixed Gibbs sampling algorithm
• Gibbs sampling algorithm
• Metropolis–Hastings algorithm