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Statistical Modeling for Degradation Data de Ding-Geng (Din) Chen

Statistical Modeling for Degradation Data: ICSA Book Series in Statistics

Editat de Ding-Geng (Din) Chen, Yuhlong Lio, Hon Keung Tony Ng, Tzong-Ru Tsai
en Limba Engleză Hardback – 14 sep 2017
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.


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Specificații

ISBN-13: 9789811051937
ISBN-10: 9811051933
Pagini: 376
Ilustrații: XVIII, 376 p. 109 illus., 67 illus. in color.
Dimensiuni: 155 x 235 x 26 mm
Ediția:1st ed. 2017
Editura: Springer Nature Singapore
Colecția Springer
Seria ICSA Book Series in Statistics

Locul publicării:Singapore, Singapore

Cuprins

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 Fréchet 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. 

Notă biografică

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. 

Textul de pe ultima copertă

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.


Caracteristici

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