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Statistical modeling for degradation...

Chen, Ding-Geng (Din).

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Statistical modeling for degradation data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical modeling for degradation data/ edited by Ding-Geng (Din) Chen ... [et al.].
其他作者:	Chen, Ding-Geng (Din).
出版者:	Singapore :Springer Singapore : : 2017.,
面頁冊數:	xviii, 376 p. :ill., digital ;24 cm.
內容註:	I. Review and Theoretical Framework -- Chapter 1: Stochastic Accelerated Degradation Models Based on a Generalized Cumu-lative Damage Approach -- Chapter 2: Hierarchical Bayesian Change-Point Analysis for Nonlinear Degradation Data -- Chapter 3: Degradation Modeling, Analysis, and Applications on Residual Life Predic-tion -- Chapter 4: On Some Shock Models with Poisson and Generalized Poisson Shock Processes -- Chapter 5: Degradation Based Reliability Modeling and Assessment of Complex Systems in Dynamic Environments -- Chapter 6: A Survey of the Modeling and Applications on Non-Destructive and De-structive Degradation Tests -- II. Modeling and Experimental Designs -- Chapter 7: Degradation Test Plan for a Nonlinear Random-Coefficients Model -- Chapter 8: Optimal Designs for LED Degradation Modeling -- Chapter 9: Gamma Degradation Models: Inferences and Optimal Designs -- Chapter 10: Model Misspecification analysis of Inverse Gaussian and Gamma Degrada-tion Processes -- III. Applications -- Chapter 11: Practical Application of Frechet Shock-Degradation Models for System Failures -- Chapter 12: Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data -- Chapter 13: Inference on Remaining Useful Life Under Gamma Degradation Models with Random effects -- Chapter 14: ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data -- Chapter 15: Modeling and Inference of CD4 Data.
Contained By:	Springer eBooks
標題:	Mathematical statistics. -
電子資源:	http://dx.doi.org/10.1007/978-981-10-5194-4
ISBN:	9789811051944

Statistical modeling for degradation data
Statistical modeling for degradation data[electronic resource] /edited by Ding-Geng (Din) Chen ... [et al.]. - Singapore :Springer Singapore :2017. - xviii, 376 p. :ill., digital ;24 cm. - ICSA book series in statistics,2199-0980. - ICSA book series in statistics..

I. Review and Theoretical Framework -- Chapter 1: Stochastic Accelerated Degradation Models Based on a Generalized Cumu-lative Damage Approach -- Chapter 2: Hierarchical Bayesian Change-Point Analysis for Nonlinear Degradation Data -- Chapter 3: Degradation Modeling, Analysis, and Applications on Residual Life Predic-tion -- Chapter 4: On Some Shock Models with Poisson and Generalized Poisson Shock Processes -- Chapter 5: Degradation Based Reliability Modeling and Assessment of Complex Systems in Dynamic Environments -- Chapter 6: A Survey of the Modeling and Applications on Non-Destructive and De-structive Degradation Tests -- II. Modeling and Experimental Designs -- Chapter 7: Degradation Test Plan for a Nonlinear Random-Coefficients Model -- Chapter 8: Optimal Designs for LED Degradation Modeling -- Chapter 9: Gamma Degradation Models: Inferences and Optimal Designs -- Chapter 10: Model Misspecification analysis of Inverse Gaussian and Gamma Degrada-tion Processes -- III. Applications -- Chapter 11: Practical Application of Frechet Shock-Degradation Models for System Failures -- Chapter 12: Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data -- Chapter 13: Inference on Remaining Useful Life Under Gamma Degradation Models with Random effects -- Chapter 14: ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data -- Chapter 15: Modeling and Inference of CD4 Data.

This book focuses on the statistical aspects of the analysis of degradation data. In recent years, degradation data analysis has come to play an increasingly important role in different disciplines such as reliability, public health sciences, and finance. For example, information on products' reliability can be obtained by analyzing degradation data. In addition, statistical modeling and inference techniques have been developed on the basis of different degradation measures. The book brings together experts engaged in statistical modeling and inference, presenting and discussing important recent advances in degradation data analysis and related applications. The topics covered are timely and have considerable potential to impact both statistics and reliability engineering.

ISBN: 9789811051944

Standard No.: 10.1007/978-981-10-5194-4doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

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505 0 $a I. Review and Theoretical Framework -- Chapter 1: Stochastic Accelerated Degradation Models Based on a Generalized Cumu-lative Damage Approach -- Chapter 2: Hierarchical Bayesian Change-Point Analysis for Nonlinear Degradation Data -- Chapter 3: Degradation Modeling, Analysis, and Applications on Residual Life Predic-tion -- Chapter 4: On Some Shock Models with Poisson and Generalized Poisson Shock Processes -- Chapter 5: Degradation Based Reliability Modeling and Assessment of Complex Systems in Dynamic Environments -- Chapter 6: A Survey of the Modeling and Applications on Non-Destructive and De-structive Degradation Tests -- II. Modeling and Experimental Designs -- Chapter 7: Degradation Test Plan for a Nonlinear Random-Coefficients Model -- Chapter 8: Optimal Designs for LED Degradation Modeling -- Chapter 9: Gamma Degradation Models: Inferences and Optimal Designs -- Chapter 10: Model Misspecification analysis of Inverse Gaussian and Gamma Degrada-tion Processes -- III. Applications -- Chapter 11: Practical Application of Frechet Shock-Degradation Models for System Failures -- Chapter 12: Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data -- Chapter 13: Inference on Remaining Useful Life Under Gamma Degradation Models with Random effects -- Chapter 14: ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data -- Chapter 15: Modeling and Inference of CD4 Data.
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