A generalization of Basu-Dhar's bivariate geometric distribution to the trivariate case. Issue 12 (2nd December 2021)

• Record Type: Journal Article
• Authors: de Oliveira, Ricardo Puziol; Mazucheli, Josmar; Achcar, Jorge Alberto
• Is Part Of: Communications in statistics. Volume 50:Issue 12(2021)
• Page Start: 4388
• Page End: 4412
• Publication Date: 2021-12-02
• Subjects: Basu-Dhar bivariate distribution -- Bayesian estimation -- Maximum likelihood estimation -- Monte Carlo simulations -- Right-censoring -- Survival analysis -- Trivariate geometric distribution; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2019.1643881
• Languages: English
• ISSNs: 0361-0918

Abstract

Abstract: In this paper, it is introduced a new parametric distribution to be used in multivariate lifetime data analysis as an alternative for the use of some existing multivariate parametric models as the popular multivariate normal distribution which is the most widely used model assumed in the analysis of continuous multivariate data. Although the normal multivariate distribution has univariate marginal normal probability distributions and simple interpretations for all their parameters, it may not be well fitted by many data sets, especially in survival data applications, usually considering logarithm transformed data. In many cases the use of parametric multivariate discrete models could be more appropriate for the data analysis. In this paper, it is introduced a generalization of the bivariate Basu-Dhar geometric distribution to a trivariate case applied to count data. Some properties of this trivariate geometric distribution, including its marginal probability distributions, order statistics distributions, the probability generating function and some simulation studies are presented. It is also presented some discussion on an extension of the trivariate case for the multivariate case. Classical and Bayesian inferences are presented assuming censored or uncensored observations. To illustrate the proposed methodology, two applications with real lifetime data are considered as examples.