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Statistical Modeling for Degradation Data

  • Book
  • © 2017

Overview

Editors:
  1. Ding-Geng (Din) Chen

    1. School of Social Work & Department of Biostatistics, Department of Statistics, University of North Carolina, University of Pretoria, Chapel Hill, USA

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  2. Yuhlong Lio

    1. Department of Mathematical Sciences, University of South Dakota, Vermillion, USA

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  3. Hon Keung Tony Ng

    1. Department of Statistical Science, Southern Methodist University, Dallas, USA

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  4. Tzong-Ru Tsai

    1. Department of Statistics, Tamkang University, New Taipei City, Taiwan

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  • All authors are experts engaged in statistical modeling in degradation data analysis
  • Timely discussions of and presentations on methodological developments and real-world applications
  • Data and computer programs will be made publicly available, allowing readers to replicate the model development
  • Presents new, high-impact methods that are readily adoptable and extendable

Part of the book series: ICSA Book Series in Statistics (ICSABSS)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Review and Theoretical Framework

    1. Front Matter

      Pages 1-1
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    2. Stochastic Accelerated Degradation Models Based on a Generalized Cumulative Damage Approach

      • Chanseok Park
      Pages 3-20

    3. Hierarchical Bayesian Change-Point Analysis for Nonlinear Degradation Data

      • Suk Joo Bae, Tao Yuan
      Pages 21-41

    4. Degradation Modeling, Analysis, and Applications on Lifetime Prediction

      • Lirong Hu, Lingjiang Li, Qingpei Hu
      Pages 43-66

    5. On Some Shock Models with Poisson and Generalized Poisson Shock Processes

      • Ji Hwan Cha, Maxim Finkelstein
      Pages 67-79

    6. Degradation-Based Reliability Modeling of Complex Systems in Dynamic Environments

      • Weiwen Peng, Lanqing Hong, Zhisheng Ye
      Pages 81-103

    7. A Survey of Modeling and Application of Non-destructive and Destructive Degradation Tests

      • Chih-Chun Tsai, Chien-Tai Lin, N. Balakrishnan
      Pages 105-124

  3. Modeling and Experimental Designs

    1. Front Matter

      Pages 125-125
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    2. Degradation Test Plan for a Nonlinear Random-Coefficients Model

      • Seong-Joon Kim, Suk Joo Bae
      Pages 127-147

    3. Optimal Designs for LED Degradation Modeling

      • Tzong-Ru Tsai, Yuhlong Lio, Nan Jiang, Hon Keung Tony Ng, Ding-Geng (Din) Chen
      Pages 149-170

    4. Gamma Degradation Models: Inference and Optimal Design

      • N. Balakrishnan, Chih-Chun Tsai, Chien-Tai Lin
      Pages 171-191

    5. Misspecification Analysis of Gamma with Inverse Gaussian Degradation Processes

      • Sheng-Tsaing Tseng, Yu-Cheng Yao
      Pages 193-208

  4. Applications

    1. Front Matter

      Pages 209-209
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    2. Practical Applications of a Family of Shock-Degradation Failure Models

      • Mei-Ling T. Lee, G. A. Whitmore
      Pages 211-229

    3. Statistical Methods for Thermal Index Estimation Based on Accelerated Destructive Degradation Test Data

      • Yimeng Xie, Zhongnan Jin, Yili Hong, Jennifer H. Van Mullekom
      Pages 231-251

    4. Inference on Remaining Useful Life Under Gamma Degradation Models with Random Effects

      • Man Ho Ling, Hon Keung Tony Ng, Kwok-Leung Tsui
      Pages 253-266

    5. ADDT: An R Package for Analysis of Accelerated Destructive Degradation Test Data

      • Zhongnan Jin, Yimeng Xie, Yili Hong, Jennifer H. Van Mullekom
      Pages 267-291

    6. Modeling and Inference of CD4 Data

      • Shuang He, Chuanhai Liu, Xiao Wang
      Pages 293-306

    7. State Space Models Based Prognostic Methods for Remaining Useful Life Prediction of Rechargeable Batteries

      • Dong Wang, Kwok-Leung Tsui
      Pages 307-334
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Keywords

About this book

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.


Editors and Affiliations

  • School of Social Work & Department of Biostatistics, Department of Statistics, University of North Carolina, University of Pretoria, Chapel Hill, USA

    Ding-Geng (Din) Chen

  • Department of Mathematical Sciences, University of South Dakota, Vermillion, USA

    Yuhlong Lio

  • Department of Statistical Science, Southern Methodist University, Dallas, USA

    Hon Keung Tony Ng

  • Department of Statistics, Tamkang University, New Taipei City, Taiwan

    Tzong-Ru Tsai

About the editors

Professor Chen is a fellow of the American Statistical Association and currently the Wallace Kuralt distinguished professor at the University of North Carolina at Chapel Hill, USA, and an extraordinary professor at University of Pretoria, South Africa. He was a professor at the University of Rochester and the Karl E. Peace endowed eminent scholar chair in biostatistics at Georgia Southern University. He is also a senior consultant for biopharmaceuticals and government agencies with extensive expertise in clinical trial biostatistics and public health statistics. Professor Chen has written more than 150 referred publications and co-authored/co-edited twelve books on clinical trial methodology with R and SAS, meta-analysis using R, advanced statistical causal-inference modeling, Monte-Carlo simulations, advanced public health statistics and statistical models in data science. 




Professor Lio is a professor at the University of South Dakota
.  He has been invited nationally and internationally to give speeches on his research, and has produced more than 70 peer-reviewed professional publications in the areas of survival analysis, computational statistics and industrial statistics (including quality control, life test, degradation modeling, etc.)  



Professor Ng is a professor at the Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA. He is currently an Associate Editor of Communications in Statistics, Computational Statistics, Journal of Statistical Computation and Simulation, and Statistics and Probability Letters. Professor Ng has more than 100 peer-reviewed professional publications to his credit, and has co-authored and co-edited two books in the areas of nonparametric methods, ordered data analysis, reliability, censoring methodology, and statistical inference. Professor Ng is an elected member of the International Statistical Institut
e and an elected senior member of the Institute of Electrical and Electronics Engineers (IEEE).  



Professor Tsai is a professor at the Department of Statistics at Tamkang University. His main research interests include quality control and reliability analysis. He previously served as a consultant for electronics companies and research institutes in Taiwan, and he has written more than 60 peer-reviewed professional publications in the areas of quality control and reliability applications. 

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