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Statistical Modeling for Degradation Data

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Cover of 'Statistical Modeling for Degradation Data'

Table of Contents

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    Book Overview
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    Chapter 1 Stochastic Accelerated Degradation Models Based on a Generalized Cumulative Damage Approach
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    Chapter 2 Hierarchical Bayesian Change-Point Analysis for Nonlinear Degradation Data
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    Chapter 3 Degradation Modeling, Analysis, and Applications on Lifetime Prediction
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    Chapter 4 On Some Shock Models with Poisson and Generalized Poisson Shock Processes
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    Chapter 5 Degradation-Based Reliability Modeling of Complex Systems in Dynamic Environments
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    Chapter 6 A Survey of Modeling and Application of Non-destructive and Destructive Degradation Tests
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    Chapter 7 Degradation Test Plan for a Nonlinear Random-Coefficients Model
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    Chapter 8 Optimal Designs for LED Degradation Modeling
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    Chapter 9 Gamma Degradation Models: Inference and Optimal Design
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    Chapter 10 Misspecification Analysis of Gamma with Inverse Gaussian Degradation Processes
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    Chapter 11 Practical Applications of a Family of Shock-Degradation Failure Models
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    Chapter 12 Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data
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    Chapter 13 Inference on Remaining Useful Life Under Gamma Degradation Models with Random Effects
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    Chapter 14 ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data
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    Chapter 15 Modeling and Inference of CD4 Data
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    Chapter 16 State Space Models Based Prognostic Methods for Remaining Useful Life Prediction of Rechargeable Batteries
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    Chapter 17 On System Identification for Accelerated Destructive Degradation Testing of Nonlinear Dynamic Systems
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Title
Statistical Modeling for Degradation Data
Published by
Springer, Singapore, January 2017
DOI 10.1007/978-981-10-5194-4
ISBNs
978-9-81-105193-7, 978-9-81-105194-4
Editors

Ding-Geng (Din) Chen, Yuhlong Lio, Hon Keung Tony Ng, Tzong-Ru Tsai

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 19%
Student > Doctoral Student 2 13%
Student > Master 2 13%
Student > Ph. D. Student 2 13%
Professor 1 6%
Other 3 19%
Unknown 3 19%
Readers by discipline Count As %
Engineering 5 31%
Computer Science 1 6%
Agricultural and Biological Sciences 1 6%
Mathematics 1 6%
Psychology 1 6%
Other 3 19%
Unknown 4 25%