Download 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.)

This volume—dedicated to William Q. Meeker at the celebration of his 60th birthday—is a suite of invited chapters protecting contemporary advances in speeded up lifestyles trying out and degradation versions. The e-book covers quite a lot of functions to parts resembling reliability, qc, the future health sciences, economics, and finance.

Specific issues lined include:

* speeded up checking out and inference

* Step-stress checking out and inference

* Nonparametric inference

* version validity in speeded up testing

* the purpose technique approach

* Bootstrap equipment in degradation analysis

* unique inferential tools in reliability

* Dynamic perturbed systems

* Degradation types in statistics

Advances in Degradation Modeling is a superb reference for researchers and practitioners in utilized likelihood and data, business statistics, the future health sciences, quality controls, economics, and finance.

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Extra resources for Advances in Degradation Modeling: Applications to Reliability, Survival Analysis, and Finance

Example text

Nelson [40], originally published in 1990, is the most important reference on the statistical aspects of accelerated testing. This book describes models, statistical methods for analysis, and statistical methods for test planning. It is sometimes said, jokingly, that engineers are very good at conducting accelerated tests that cause units to fail quickly. Hitting a sample of integrated circuits with a hammer will certainly make them fail, but will provide no useful information about product reliability.

Cumulative hazard functions of cumulative exposure and cumulative risk models . . . . . . . . . . . . . . . . . . . . . . . . Empirical and predicted CH functions: test group . . . . . . . Sampling distribution of the MLE of a . . . . . . . . . . . . Sampling distribution of the MLE of b . . . . . . . . . . . . Sampling distribution of the MLE of θ1 . . . . . . . . . . . . Sampling distribution of the MLE of θ2 .

The application described in Meeker, Escobar, and Lu [33] is one such application. In these examples, the limited number of failures provided only limited reliability information and the results of a degradation analysis were more informative. When an appropriate degradation variable can be measured, degradation data, when properly analyzed, can provide much more information because there are quantitative measurements on all units (not just those that failed). Indeed, it is possible to make powerful reliability inferences from degradation data even when there are no failures.

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