A Bayesian analysis for the bivariate geometric distribution in the presence of covariates and censored data

Naser Davarzani*, J. A. Achcar, Ralf Peeters, Evgueni Smirnov

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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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.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalJournal of Statistics and Management Systems
Issue number1
Publication statusPublished - 2017


  • Bayesian analysis
  • Bivariate geometric distribution
  • Censored lifetimes
  • Covariates
  • Lifetime data
  • MCMC methods

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