Inverse Gaussian process models for bivariate degradation analysis: a Bayesian perspective. (English) Zbl 1392.62070
Summary: 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.
MSC:
| 62F15 | Bayesian inference |
| 62P30 | Applications of statistics in engineering and industry; control charts |
Keywords:
bivariate degradation; Bayesian MCMC method; copula function; inverse Gaussian process; random effectsReferences:
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