Statistical Decision Problems: Selected Concepts and Portfolio Safeguard Case Studies

ISBN-13: 9781493953257 / Angielski / Miękka / 2016 / 249 str.

Michael Zabarankin; Stanislav Uryasev

Opis

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.

Recenzje

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)

Spis treści

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.​

Blurb

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.