Overview
- 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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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.
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.
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Keywords
Table of contents (17 chapters)
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Front Matter
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Review and Theoretical Framework
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Front Matter
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Applications
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Front Matter
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Editors and Affiliations
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.
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.
Bibliographic Information
Book Title: Statistical Modeling for Degradation Data
Editors: Ding-Geng (Din) Chen, Yuhlong Lio, Hon Keung Tony Ng, Tzong-Ru Tsai
Series Title: ICSA Book Series in Statistics
DOI: https://doi.org/10.1007/978-981-10-5194-4
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2017
Hardcover ISBN: 978-981-10-5193-7Published: 14 September 2017
Softcover ISBN: 978-981-13-5340-6Published: 12 December 2018
eBook ISBN: 978-981-10-5194-4Published: 31 August 2017
Series ISSN: 2199-0980
Series E-ISSN: 2199-0999
Edition Number: 1
Number of Pages: XVIII, 376
Number of Illustrations: 42 b/w illustrations, 67 illustrations in colour
Topics: Statistical Theory and Methods, Statistics for Business, Management, Economics, Finance, Insurance