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

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 party of his 60th birthday—is a suite of invited chapters protecting contemporary advances in speeded up lifestyles trying out and degradation types. The ebook covers a variety of functions to parts corresponding to reliability, qc, the future health sciences, economics, and finance.

Specific themes coated include:

* sped up trying out and inference

* Step-stress trying out and inference

* Nonparametric inference

* version validity in sped up testing

* the purpose technique approach

* Bootstrap tools in degradation analysis

* unique inferential tools in reliability

* Dynamic perturbed systems

* Degradation versions in statistics

Advances in Degradation Modeling is a superb reference for researchers and practitioners in utilized chance and data, commercial information, the healthiness sciences, qc, economics, and finance.

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

Sample text

Definition of time transformation function . . . . . . . . . . . Definition of the studied test plan . . . . . . . . . . . . . . Record of failure times . . . . . . . . . . . . . . . . . . . Reliability functions by the different ALT models (N in hours) . Reliability function by parametric model (N in hours) . . . . . Reliability function by GPH model (N in hours) . . . . . . . . 6 Reliability function by semi-parametric model (N in hours) .

Parameter setting for examining properties of estimators (two failure modes) . . . . . . . . . . . . . . . . . . . . . . . . . . Asymptotic variances of parameters (two failure modes) . . . . 2)) . . . . . . . . . . . . . . . 2)) . . . . . . . . . . . . . . . Mean-squared errors of maximum likelihood estimators in 1000 simulation runs . . . . . . . . . . . . . . . . . . . . . . Pseudo failure distances .

Confidence estimates for θY = (μ, β) and θT = (α, γ) . . . . . . Sample for observations of process increments for the case n = 2, m1 = 3, m2 = 2 . . . . . . . . . . . . . . . . . . . . . . Confidence estimates for (μ, σ 2 ) . . . . . . . . . . . . . . . Confidence estimates for (μ, σ 2 ) . . . . . . . . . . . . . . . Confidence estimates for (μ, h) . . . . . . . . . . . . . . . . Confidence estimates for (σ 2 , h) . . . . .

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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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