Monte-Carlo Simulation-Based Statistical Modeling

John Dean Chen

ISBN 10: 9811033064 / ISBN 13: 9789811033063

Edité par Springer Nature Singapore, Springer Nature Singapore Feb 2017, 2017

A propos de cet article

Neuware -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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 452 pp. Englisch.

N° de réf. du vendeur 9789811033063

Détails bibliographiques

• Titre: Monte-Carlo Simulation-Based Statistical Modeling
• Éditeur: Springer Nature Singapore, Springer Nature Singapore Feb 2017
• Date de parution: 2017
• Langue: anglais
• ISBN à 10 chiffres: 9811033064
• ISBN à 13 chiffres: 9789811033063
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

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.

À propos de l?auteur

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.