Inverse Gaussian Processes With Random Effects and Explanatory Variables for Degradation Data

Degradation models are widely used to assess the lifetime information of highly reliable products. This study proposes a degradation model based on an inverse normal-gamma mixture of an inverse Gaussian process. This article presents the properties of the lifetime distribution and parameter estimation using the EM-type algorithm, in addition to providing a simple model-checking procedure to assess the validity of different stochastic processes. Several case applications are performed to demonstrate the advantages of the proposed model with random effects and explanatory variables. Technical details, data, and R code are available online as supplementary materials.