期刊
JOURNAL OF MULTIVARIATE ANALYSIS
卷 101, 期 2, 页码 340-351出版社
ELSEVIER INC
DOI: 10.1016/j.jmva.2008.12.007
关键词
Bootstrap; Degradation; EM algorithm; Empirical processes; Random effects; Reliability; Wiener process
资金
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [0805031] Funding Source: National Science Foundation
This article studies the maximum likelihood inference on a class of Wiener processes with random effects for degradation data. Degradation data are special case of functional data with monotone trend. The setting for degradation data is one on which n independent subjects, each with a Wiener process with random drift and diffusion parameters, are observed at possible different times. Unit-to-unit variability is incorporated into the model by these random effects. EM algorithm is used to obtain the maximum likelihood estimators of the unknown parameters. Asymptotic properties such as consistency and convergence rate are established. Bootstrap method is used for assessing the uncertainties of the estimators. Simulations are used to validate the method The model is fitted to. bridge beam data and corresponding goodness-of-fit tests are carried out. Failure time distributions in terms of degradation level passages are calculated and illustrated. (C) 2008 Elsevier Inc. All rights reserved.
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