An Inverse Gaussian Process Model for Degradation Data

This paper studies the maximum likelihood estimation of a class of inverse Gaussian process models for degradation data. Both the subject-to-subject heterogeneity and covariate information can be incorporated into the model in a natural way. The EM algorithm is used to obtain the maximum likelihood estimators of the unknown parameters and the bootstrap is used to assess the variability of the maximum likelihood estimators. Simulations are used to validate the method. The model is fitted to laser data and corresponding goodness-of-fit tests are carried out. Failure time distributions in terms of degradation level passages are calculated and illustrated. The supplemental materials for this article are available online.