ANALYSIS OF JOINT MULTIPLY TYPE-II CENSORED DATA USING THE GIBBS SAMPLER ALGORITHM

Abstract

This paper introduces a systematic approach for analyzing data under joint multiply type-II censoring. The study assumes a one-parameter exponential lifetime distribution and focuses on estimating unknown parameters. The maximum likelihood method is used to obtain frequentist point estimates, while a Bayesian framework is adopted to draw the corresponding Bayes inferences. To effectively handle censored data, an extended Gibbs sampler algorithm is employed, treating the unknown observations as further unknowns and estimating them accordingly. This methodology ensures a comprehensive and robust inference process by simultaneously addressing parameter uncertainty and the challenges posed by the censored observations. © 2025, Gnedenko Forum. All rights reserved.