Fode, Zhang, Ng, Hon Keung Tony, and Shen, Lijuan (2024) Robust Estimation and Selection for Degradation Modeling With Inhomogeneous Increments. IEEE Transactions on Reliability, 73 (1). pp. 560-575.

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Abstract

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.

Item ID:	93143
Item Type:	Article (Research - C1)
ISSN:	1558-1721
Copyright Information:	Copyright © 2024, IEEE.
Date Deposited:	07 Aug 2026 07:18
FoR Codes:	49 MATHEMATICAL SCIENCES > 4905 Statistics > 490508 Statistical data science @ 70% 40 ENGINEERING > 4010 Engineering practice and education > 401006 Systems engineering @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280118 Expanding knowledge in the mathematical sciences @ 30% 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280110 Expanding knowledge in engineering @ 70%
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