An Inverse Gaussian Process Model for Degradation Data

被引：343

作者：

Wang, Xiao ^{[1
]}

Xu, Dihua ^{[2
]}

机构：

[1] Purdue Univ, Dept Stat, W Lafayette, IN 47907 USA

[2] Univ Maryland Baltimore Cty, Dept Math & Stat, Baltimore, MD 21250 USA

来源：

TECHNOMETRICS | 2010年 / 52卷 / 02期

基金：

美国国家科学基金会;

关键词：

Bootstrap; Degradation data; EM algorithm; Empirical processes; Random effects; Reliability; GAMMA PROCESS MODEL;

D O I：

10.1198/TECH.2009.08197

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

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.

引用

页码：188 / 197

页数：10

共 27 条

[21] Semiparametric inference on a class of Wiener processes [J].