Monte Carlo Markov Chain (MCMC) Approach to Estimate Parameters of Burr Type III Distribution

• Activity Number: 343 - Contributed Poster Presentations: Section on Bayesian Statistical Science
• Type: Contributed
• Date/Time: Tuesday, August 9, 2022 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Woosuk Kim and Elijah Adam McClymonds* and Aran Bybee and Alyssa Kasmierski
• Companies: Slippery Rock University and Slippery Rock University and Slippery Rock University and Slippery Rock University

Abstract

In 1942, Burr type III distribution was first introduced by I. W. Burr for modelling lifetime data and survival data. In 1996, S. R. Lindsay introduced Burr type III distribution with location and scale parameters. In statistics, parameter estimation is one of the many main purposes in determining descriptive measures of an entire population. There are many statistical procedures to estimate parameters. Monte Carlo Markov Chain procedures, such as Gibbs sampling and Metropolis-Hastings algorithm, are very popular to sample from arbitrary distributions. From the sample, we can get the sample mean, which is the estimate of a parameter. In this project, we want to estimate parameters of Burr type III distribution by Monte Carlo Markov Chain Procedure.