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＜電子ブック＞
Statistical Decision Problems : Selected Concepts and Portfolio Safeguard Case Studies / by Michael Zabarankin, Stan Uryasev
(Springer Optimization and Its Applications. ISSN:19316836 ; 85)

版	1st ed. 2014.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2014
本文言語	英語
大きさ	XIV, 249 p. 9 illus., 4 illus. in color : online resource
著者標目	*Zabarankin, Michael author Uryasev, Stan author SpringerLink (Online service)
件　名	LCSH:Operations research LCSH:Management science LCSH:Probabilities LCSH:Data mining LCSH:Mathematical optimization FREE:Operations Research, Management Science FREE:Probability Theory FREE:Data Mining and Knowledge Discovery FREE:Optimization FREE:Operations Research and Decision Theory
一般注記	1. Random Variables -- 2. Deviation, Risk, and Error Measures -- 3. Probabilistic Inequalities -- 4. Maximum Likelihood Method -- 5. Entropy Maximization -- 6. Regression Models -- 7. Classification -- 8. Statistical Decision Models with Risk and Deviation -- 9. Portfolio Safeguard Case Studies -- Index -- References 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 HTTP:URL=https://doi.org/10.1007/978-1-4614-8471-4

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