Overview
- Presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems
- Discusses basic principles of statistical decision making from optimization perspective in various risk management applications such as optimal hedging, portfolio optimization, portfolio replication, and more
- Introduces state-of-the-art practical decision making through seventeen case studies from real-life applications?
Part of the book series: Springer Optimization and Its Applications (SOIA, volume 85)
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Table of contents (9 chapters)
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Front Matter
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Selected Concepts of Statistical Decision Theory
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Front Matter
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Statistical Decision Problems
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Front Matter
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Portfolio Safeguard Case Studies
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Front Matter
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Back Matter
Keywords
About this book
Statistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more.
The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications.
Reviews
From the book reviews:
“The book offers a chapter-length primer on probability and statistical risk (section I), followed by a review of standard problems and procedures, all from a statistical decision theory viewpoint (section II). The heart of the book is section III, which shows in detail how to handle many such problems using the Portfolio Safeguard software package. … The book will mostly benefit readers who use or consider using Portfolio Safeguard and are looking for a complementary, textbook-style treatment.” (Jörg Stoye, zbMATH, Vol. 1291, 2014)Authors and Affiliations
Bibliographic Information
Book Title: Statistical Decision Problems
Book Subtitle: Selected Concepts and Portfolio Safeguard Case Studies
Authors: Michael Zabarankin, Stan Uryasev
Series Title: Springer Optimization and Its Applications
DOI: https://doi.org/10.1007/978-1-4614-8471-4
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, New York 2014
Hardcover ISBN: 978-1-4614-8470-7Published: 17 December 2013
Softcover ISBN: 978-1-4939-5325-7Published: 19 August 2016
eBook ISBN: 978-1-4614-8471-4Published: 16 December 2013
Series ISSN: 1931-6828
Series E-ISSN: 1931-6836
Edition Number: 1
Number of Pages: XIV, 249
Number of Illustrations: 5 b/w illustrations, 4 illustrations in colour
Topics: Operations Research, Management Science, Probability Theory and Stochastic Processes, Data Mining and Knowledge Discovery, Optimization, Operations Research/Decision Theory