A Bayesian analysis for the bivariate geometric distribution in the presence of covariates and censored data. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Davarzani, Nasser; Achcar, Jorge Alberto; Peeters, Ralf; Smirnov, Evgueni Nikolaevich
• Is Part Of: Journal of statistics & management systems. Volume 20:Issue 1(2017)
• Page Start: 1
• Page End: 16
• Publication Date: 2017-01-02
• Subjects: Bivariate geometric distribution -- Lifetime data -- Bayesian analysis -- MCMC methods -- Covariates -- Censored lifetimes; Statistics -- Periodicals; Mathematical models -- Periodicals; Mathematical models; Statistics; Periodicals; 519.5
• DOI: 10.1080/09720510.2016.1160624
• Languages: English
• ISSNs: 0972-0510

Abstract

Abstract: In this paper, we introduce a Bayesian analysis for bivariate geometric distributions applied to lifetime data in the presence of covariates and censored data using Markov Chain Monte Carlo (MCMC) methods. We show that the use of a discrete bivariate geometric distribution could bring us some computational advantages when compared to standard existing bivariate exponential lifetime distributions introduced in the literature assuming continuous lifetime data as for example, the exponential Block and Basu bivariate distribution. Posterior summaries of interest are obtained using the popular OpenBUGS software. A numerical illustration is introduced considering a medical data set related to the recurrence times of infection for kidney patients.