Statistical Analysis for Competing Risks' Model with Two Dependent Failure Modes from Marshall–Olkin Bivariate Gompertz Distribution. (28th May 2022)

• Record Type: Journal Article
• Authors: Wu, Min; Zhang, Fode; Shi, Yimin; Wang, Yan
• Other Names: Kumar Akshi Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-05-28
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/3988225
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : The bivariate or multivariate distribution can be used to account for the dependence structure between different failure modes. This paper considers two dependent competing failure modes from Gompertz distribution, and the dependence structure of these two failure modes is handled by the Marshall–Olkin bivariate distribution. We obtain the maximum likelihood estimates (MLEs) based on classical likelihood theory and the associated bootstrap confidence intervals (CIs). The posterior density function based on the conjugate prior and noninformative (Jeffreys and Reference) priors are studied; we obtain the Bayesian estimates in explicit forms and construct the associated highest posterior density (HPD) CIs. The performance of the proposed methods is assessed by numerical illustration.