The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Survival in Dynamic Environments
Open accessAbstract
This expository paper is an overview of a relatively new class of failure models, both univariate and multivariate, that are suitable for describing the lifelength of items that operate in dynamic environments. Many of the currently used failure models are developed under the premise that the operating environment is static: these models turn out to be special cases of the new models that are overviewed here. These new models are derived by describing the underlying failure-causing mechanisms, such as degradation and wear, using suitable stochastic processes: this is the underlying mathematical theme that derives their development. Because of their generality, the new models should provide improved descriptions of failure data and assessments of item survivability. Furthermore, they may signal a new philosophy of life-testing experiments wherein one also monitors the environmental factors that govern the tests. This overview categorizes the models by the various strategies used for their development, outlines the salient assumptions underlying them and provides a convenient road map to the pertinent references, which are scattered among the applied probability, engineering, reliability and survival analysis literatures. It is our hope that this paper sets a tone for the direction of future work in the development of models for survival wherein the physics of failure and the characteristics of the operating environment play a key role.
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Cites
Sampling-based approaches to calculating marginal densities
Gelfand, A. E., & Smith, A. F. M. (1990) · Journal of the American Statistical Association · doi:10.1080/01621459.1990.10476213 · Cited by 6,606
Cited by
Remaining useful life estimation – A review on the statistical data driven approaches
Si, X.-S., Wang, W., Hu, C.-H., & Zhou, D.-H. (2011) · European Journal of Operational Research · doi:10.1016/j.ejor.2010.11.018 · Cited by 2,162
The Inverse Gaussian Process as a Degradation Model
Ye, Z.-S., & Chen, N. (2014) · Technometrics · doi:10.1080/00401706.2013.830074 · Cited by 560
An Inverse Gaussian Process Model for Degradation Data
Wang, X., & Xu, D. (2010) · Technometrics · doi:10.1198/tech.2009.08197 · Cited by 395
Metadata
- Authors
- Singpurwalla, N. D.
- Venue
- Statistical Science
- Publisher
- Institute of Mathematical Statistics
- Published
- 1995-02-01
- Volume
- 10
- Issue
- 1
- Pages
- 86-103
- ISSN
- 0883-4237 (ISSN-L)
- OpenAlex
- W2023344466
- Cited by
- 488
- References
- 1
- FWCI
- 18.37
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Singpurwalla, N. D. (1995). Survival in Dynamic Environments. Statistical Science. https://doi.org/10.1214/ss/1177010132
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.