Robust Estimation and Selection for Degradation Modeling With Inhomogeneous Increments

The evaluation of long-lifetime and high-reliability products has attracted much attention. Stochastic degradation modeling is one of the most popular methods. The classical stochastic processes are frequently employed to discuss degradation trajectories. Most current work assumes that the underlying probability model of a degradation process is known or fixed in the estimation and model selection procedures. However, the ground-truth degradation model is usually unavailable in engineering applications. This article proposes a feasible parameter estimation and model selection procedure by measuring the distribution divergence among the nonparametric estimated model and some candidate models. In the proposed methods, it is not necessary to assume the availability of a ground-true model, which is replaced by a nonparametric estimated model. The proposed methodologies are suitable for restricted independent and nonidentically distributed samples. We discuss the large sample property of the suggested estimators. We report the Monte Carlo simulation study and practical data analysis to demonstrate our methods.