The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
An Inverse Gaussian Process Model for Degradation Data
Abstract
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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Quasi-likelihood functions, generalized linear models, and the Gauss—Newton method
WEDDERBURN, R. W. M. (1974) · Biometrika · doi:10.1093/biomet/61.3.439 · Cited by 2,122
Nonparametric estimates of standard error: The jackknife, the bootstrap and other methods
EFRON, B. (1981) · Biometrika · doi:10.1093/biomet/68.3.589 · Cited by 1,399
Using Degradation Measures to Estimate a Time-to-Failure Distribution
Lu, C. J., & Meeker, W. O. (1993) · Technometrics · doi:10.1080/00401706.1993.10485038 · Cited by 1,161
The Distribution of Chi-Square
Wilson, E. B., & Hilferty, M. M. (1931) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.17.12.684 · Cited by 824
Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure
Lawless, J., & Crowder, M. (2004) · Lifetime Data Analysis · doi:10.1023/b:lida.0000036389.14073.dd · Cited by 583
Modelling Accelerated Degradation Data Using Wiener Diffusion With A Time Scale Transformation
Whitmore, G. A., & Schenkelberg, F. (1997) · Lifetime Data Analysis · doi:10.1023/a:1009664101413 · Cited by 489
Survival in Dynamic Environments
Singpurwalla, N. D. (1995) · Statistical Science · doi:10.1214/ss/1177010132 · Cited by 488
Doksum, K. A., Høyland, A., & Høyland, A. (1992) · Technometrics · doi:10.2307/1269554 · Cited by 378
Estimating degradation by a wiener diffusion process subject to measurement error
Whitmore, G. A. (1995) · Lifetime Data Analysis · doi:10.1007/bf00985762 · Cited by 354
Cited by
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.11.016 · Cited by 2,634
The Inverse Gaussian Process as a Degradation Model
Ye, Z.-S., & Chen, N. (2014) · Technometrics · doi:10.1080/00401706.2013.830074 · Cited by 560
Metadata
- Authors
- Wang, X., & Xu, D.
- Venue
- Technometrics
- Publisher
- Informa UK Limited
- Published
- 2010-05
- Volume
- 52
- Issue
- 2
- Pages
- 188-197
- ISSN
- 0040-1706 (ISSN-L)
- 1537-2723
- OpenAlex
- W1501947958
- Cited by
- 395
- References
- 13
- FWCI
- 21.01
- Type
- journal article
- Language
- en
- Open access
- closed
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