Statistical Decision Problems
Selected Concepts and Portfolio Safeguard Case Studies
Authors: Zabarankin, Michael, Uryasev, Stanislav
Free Preview- 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
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- About this book
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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
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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)
- Table of contents (9 chapters)
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Random Variables
Pages 3-17
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Deviation, Risk, and Error Measures
Pages 19-31
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Probabilistic Inequalities
Pages 33-41
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Maximum Likelihood Method
Pages 45-52
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Entropy Maximization
Pages 53-70
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Table of contents (9 chapters)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Statistical Decision Problems
- Book Subtitle
- Selected Concepts and Portfolio Safeguard Case Studies
- Authors
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- Michael Zabarankin
- Stanislav Uryasev
- Series Title
- Springer Optimization and Its Applications
- Series Volume
- 85
- Copyright
- 2014
- Publisher
- Springer-Verlag New York
- Copyright Holder
- Springer Science+Business Media, New York
- Distribution Rights
- Distribution rights for India: Delhi Book Store, New Delhi, India
- eBook ISBN
- 978-1-4614-8471-4
- DOI
- 10.1007/978-1-4614-8471-4
- Hardcover ISBN
- 978-1-4614-8470-7
- Softcover ISBN
- 978-1-4939-5325-7
- Series ISSN
- 1931-6828
- Edition Number
- 1
- Number of Pages
- XIV, 249
- Number of Illustrations
- 5 b/w illustrations, 4 illustrations in colour
- Topics
