Overview
- Editors:
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Ding-Geng (Din) Chen
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Gillings School of Global Public Health, University of North Carolina, Chapel Hill, USA
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John Dean Chen
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Risk Management, Credit Suisse Risk Management, New York, USA
- Written by experts actively engaged in Monte-Carlo simulation-based statistical modeling
- Includes timely discussions and presentations on methodological developments and concrete applications
- Introduces data and computer programs that will be made publicly available, allowing readers to replicate the model developments
- Features readily adoptable and extendable, high-impact methods
- Includes supplementary material: sn.pub/extras
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Table of contents (19 chapters)
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Monte-Carlo Techniques
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- Hakan Demirtas, Rawan Allozi, Yiran Hu, Gul Inan, Levent Ozbek
Pages 3-15
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- Jessica R. Hoag, Chia-Ling Kuo
Pages 41-57
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- Hakan Demirtas, Ceren Vardar-Acar
Pages 59-84
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- H. K. T. Ng, Y.-J. Lin, T.-R. Tsai, Y. L. Lio, N. Jiang
Pages 107-126
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Monte-Carlo Methods in Missing Data
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Front Matter
Pages 127-127
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- Haresh Rochani, Daniel F. Linder
Pages 129-142
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- A. Satty, H. Mwambi, G. Molenberghs
Pages 179-209
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Monte-Carlo in Statistical Modellings and Applications
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Front Matter
Pages 253-253
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- Kyle M. Irimata, Jeffrey R. Wilson
Pages 255-283
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- Chuanshu Ji, Tao Wang, Leicheng Yin
Pages 285-317
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- Tyler Cook, Zhigang Zhang, Jianguo Sun
Pages 319-346
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About this book
This book brings together expert researchers engaged in Monte-Carlo simulation-based statistical modeling, offering them a forum to present and discuss recent issues in methodological development as well as public health applications. It is divided into three parts, with the first providing an overview of Monte-Carlo techniques, the second focusing on missing data Monte-Carlo methods, and the third addressing Bayesian and general statistical modeling using Monte-Carlo simulations. The data and computer programs used here will also be made publicly available, allowing readers to replicate the model development and data analysis presented in each chapter, and to readily apply them in their own research. Featuring highly topical content, the book has the potential to impact model development and data analyses across a wide spectrum of fields, and to spark further research in this direction.
Editors and Affiliations
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Gillings School of Global Public Health, University of North Carolina, Chapel Hill, USA
Ding-Geng (Din) Chen
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Risk Management, Credit Suisse Risk Management, New York, USA
John Dean Chen
About the editors
Professor Ding-Geng 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. 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 statistics 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 professional publications and co-authored and co-edited eight books on clinical trial methodology, meta-analysis, causal-inference and public health statistics.
Mr. John Dean Chen is specialized in Monte-Carlo simulations in modelling financial market risk. In his career on Wall Street, he worked in Market Risk in commodities trading, structuring notes on the Exotics Interest Rate Derivatives desk at Barclays Capital. During his career in the financial industry, he witnessed in person the unfolding of the financial crisis, and the immediate aftermath consuming much of the financial industry. In its wake, a dizzying array of regulations were made from the government, severely limiting the businesses that once made banks so profitable. Mr Chen transitioned back to the Risk side of the business working in Market and Model Risk. He is currently a Vice President at Credit Suisse specializing in regulatory stress testing with Monte-Carlo simulations. He graduated from the University of Washington with a dual Bachelors of Science in Applied Mathematics and Economics.
