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Advances in Degradation Modeling: Applications to by William Q. Meeker (auth.), M.S. Nikulin, Nikolaos Limnios, PDF

By William Q. Meeker (auth.), M.S. Nikulin, Nikolaos Limnios, N. Balakrishnan, Waltraud Kahle, Catherine Huber-Carol (eds.)

ISBN-10: 0817649239

ISBN-13: 9780817649234

This volume—dedicated to William Q. Meeker at the celebration of his 60th birthday—is a set of invited chapters masking fresh advances in sped up lifestyles trying out and degradation versions. The e-book covers quite a lot of functions to components corresponding to reliability, quality controls, the well-being sciences, economics, and finance.

Specific issues coated include:

* speeded up checking out and inference

* Step-stress trying out and inference

* Nonparametric inference

* version validity in sped up testing

* the purpose method approach

* Bootstrap tools in degradation analysis

* certain inferential equipment in reliability

* Dynamic perturbed systems

* Degradation types in statistics

Advances in Degradation Modeling is a wonderful reference for researchers and practitioners in utilized chance and facts, commercial records, the overall healthiness sciences, qc, economics, and finance.

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Additional info for Advances in Degradation Modeling: Applications to Reliability, Survival Analysis, and Finance

Example text

Parameter setting for examining properties of estimators (one failure mode) . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic variances of estimators (one failure mode) . . . . . 0) . . . . . . . . . . . . . . . . 0) . . . . . . . . . . . . . . . . 7) . . . . . . . . . . . . . . . . Parameter setting for examining properties of estimators (two failure modes) . . . . . . . . . . . . . . .

In practice, however, there is not enough information to identify such a model. , to predict the effect of removing a failure mode), it is often possible to collapse a large number of related failure modes into several groups such that the failure modes are approximately independent. , p. 34 of David and Moeschberger [10]). Meeker, Escobar, and Hong [32] describe an application where many failure modes were collapsed into two groups, engineering judgment was used to make an assumption about the degree of dependency, and sensitivity analysis was used to assure that inferences were reasonably accurate over a plausible range of assumptions.

2)) . . . . . . . . . . . . . . . Mean-squared errors of maximum likelihood estimators in 1000 simulation runs . . . . . . . . . . . . . . . . . . . . . . Pseudo failure distances . . . . . . . . . . . . . . . . . . Interval and point estimates obtained by each method . . . . . Bayesian estimates of the quantities of interest and 95% HPD regions (prior: Weibull distribution) . . . . . . . . . . . . . . . . . Bayesian estimates of the quantities of interest and 95% HPD regions (prior: lognormal distribution) .

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Advances in Degradation Modeling: Applications to Reliability, Survival Analysis, and Finance by William Q. Meeker (auth.), M.S. Nikulin, Nikolaos Limnios, N. Balakrishnan, Waltraud Kahle, Catherine Huber-Carol (eds.)


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