Bayesian Analysis of Modified Weibull distribution under progressively censored competing risk model

TL;DR

This paper develops a Bayesian framework for analyzing modified Weibull distributions in a progressively censored competing risks setting, utilizing Gibbs and slice sampling methods, with real data illustration.

Contribution

It introduces a novel Bayesian approach combining Gibbs and slice sampling for modified Weibull models under progressive censoring, with application to real data.

Findings

Effective posterior sampling using combined Gibbs and slice methods

Application to real-life censored data demonstrates practical utility

Provides a new Bayesian methodology for complex survival data

Abstract

In this paper we study bayesian analysis of Modified Weibull distribution under progressively censored competing risk model. This study is made for progressively censored data. We use deterministic scan Gibbs sampling combined with slice sampling to generate from the posterior distribution. Posterior distribution is formed by taking prior distribution as reference prior. A real life data analysis is shown for illustrative purpose.