Inverse Gaussian process models for bivariate degradation analysis: A Bayesian perspective. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Duan, Fengjun; Wang, Guanjun; Wang, Huan
• Is Part Of: Communications in statistics. Volume 47:Issue 1(2018)
• Page Start: 166
• Page End: 186
• Publication Date: 2018-01-02
• Subjects: Bivariate degradation -- Bayesian MCMC method -- Copula function -- Inverse Gaussian process -- Random effects; Primary 62F15 -- Secondary 62P30; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2017.1280162
• Languages: English
• ISSNs: 0361-0918

Abstract

ABSTRACT: This article conducts a Bayesian analysis for bivariate degradation models based on the inverse Gaussian (IG) process. Assume that a product has two quality characteristics (QCs) and each of the QCs is governed by an IG process. The dependence of the QCs is described by a copula function. A bivariate simple IG process model and three bivariate IG process models with random effects are investigated by using Bayesian method. In addition, a simulation example is given to illustrate the effectiveness of the proposed methods. Finally, an example about heavy machine tools is presented to validate the proposed models.