A Bayes Analysis of Inverse Gaussian Based Accelerated Test Models

摘要

The paper considers the Bayesian analysis of an elaborated family of regression models based on the inverse Gaussian distribution, a family that is quite useful for the accelerated test scenario in life testing. A variety of relationships between the model parameters and the entertained stress variable gives rise to four different families. For the Bayesian implementation, the paper considers independent weak priors for the parameters and proposes Gibbs sampler algorithm for obtaining samples from the relevant posteriors. The implementation is also extended when the data are subject to censoring mechanism. Numerical illustration is based on a real dataset on temperature accelerated life testing. Finally, the compatibility of each entertained model is assessed and they are compared using some commonly available Bayesian toolkits. The results are found to be satisfactory.