Statistical Quality Technologies / ICSA Book Series in Statistics (PDF)
Theory and Practice
(Sprache: Englisch)
This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality...
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This book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks. Numerous statistical techniques and methodologies for quality control and improvement have been developed in recent years to help resolve current product quality issues in today's fast changing environment. Featuring contributions from top experts in the field, this book covers three major topics: statistical process control, acceptance sampling plans, and reliability testing and designs. The topics covered in the book are timely and have a high potential impact and influence to academics, scholars, students and professionals in statistics, engineering, manufacturing and health.
Autoren-Porträt
Yuhlong Lio is a professor in the Department of Mathematical Science at the University of South Dakota. His research interest is in theoretical and methodology developments in mathematics and includes reliability inferences, kernel-smooth estimation, and mathematical modeling. Dr. Lio has been invited as a referee to review papers for more than 30 international and peer-review journals including Applied Mathematics and Computation, Applied Mathematical Modeling, Journal of Quality Technology and IEEE Transactions on Reliability, among others. Dr. Lio currently serves on the advisory board for Journal of Statistics and Mathematics and as associate editor for Journal of Statistical Computation and Simulation and Electronic Journal of Applied Statistical Analysis. Dr. Lio received his BS in mathematics from Nation Cheng Kung University, Taiwan, his MS in mathematics from National Central University, Taiwan, and his PhD. in Statistics from University of South Carolina, USA.Hon Keung Tony Ng is a professor of statistical science with the Southern Methodist University, Dallas, TX, USA. He is an associate editor of Communications in Statistics, Computational Statistics, IEEE Transactions on Reliability, Journal of Statistical Computation and Simulation, Naval Research Logistics, Sequential Analysis and Statistics and Probability Letters. His research interests include reliability, censoring methodology, ordered data analysis, nonparametric methods, and statistical inference. He has published more than 100 research papers in refereed journals. He is the co-author of the book Precedence-Type Tests and Applications (2006, with Balakrishnan) and co-editor of Ordered Data Analysis, Modeling and Health Research Methods (Springer 2015, ed. with Choudhary, Nagaraja). Professor Ng is a fellow of the American Statistical Association, an elected senior member of IEEE and an elected member of the International Statistical Institute.
Tzong-Ru Tsai is the dean of the College of
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Business and Management and a professor in the Department of Statistics at Tamkang University in New Taipei City, Taiwan. His main research interests include quality control and reliability analysis. He has served as a consultant with extensive expertise in statistical quality control, reliability assessment on highly reliable products, and design of experiments for many companies in the past years. He is an associate editor of the Journal of Statistical Computation and Simulation. Dr. Tsai has been invited as a referee to review papers for more than 20 peer-review journals, including IEEE Transactions on Reliability, Quality Engineering, and Quality and Reliability Engineering International. He has published more than 70 research papers in refereed journals.
Ding-Geng (Din) Chen is the Wallace H. Kuralt Distinguished Professor and Director of the Consortium for Statistical Development and Consultation (CSDC) in the School of Social Work, and is jointly appointed as a clinical professor in the Department of Biostatistics at the UNC Gillings School of Global Health. He is an elected fellow of American Statistical Association. As a professor in biostatistics, he is interested in developing biostatistical methodologies in clinical trials, meta-analysis, Bayesian statistics and their applications to public health. As a professor in social work, he is interested in developing Bayesian social and health intervention research, cusp catastrophe modelling, statistical causal inferences, propensity score and structural-equation models (SEM). He is PI/Co-PI for several NIH R01 research projects in biostatistical methodology development and public health applications.
Ding-Geng (Din) Chen is the Wallace H. Kuralt Distinguished Professor and Director of the Consortium for Statistical Development and Consultation (CSDC) in the School of Social Work, and is jointly appointed as a clinical professor in the Department of Biostatistics at the UNC Gillings School of Global Health. He is an elected fellow of American Statistical Association. As a professor in biostatistics, he is interested in developing biostatistical methodologies in clinical trials, meta-analysis, Bayesian statistics and their applications to public health. As a professor in social work, he is interested in developing Bayesian social and health intervention research, cusp catastrophe modelling, statistical causal inferences, propensity score and structural-equation models (SEM). He is PI/Co-PI for several NIH R01 research projects in biostatistical methodology development and public health applications.
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Bibliographische Angaben
- 2019, 1st ed. 2019, 402 Seiten, Englisch
- Herausgegeben: Yuhlong Lio, Hon Keung Tony Ng, Tzong-Ru Tsai, Ding-Geng Chen
- Verlag: Springer-Verlag GmbH
- ISBN-10: 3030207099
- ISBN-13: 9783030207090
- Erscheinungsdatum: 09.08.2019
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- Größe: 5.64 MB
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