Statistical Decision Problems

Selected Concepts and Portfolio Safeguard Case Studies

  • Michael Zabarankin
  • Stan Uryasev

Part of the Springer Optimization and Its Applications book series (SOIA, volume 85)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Selected Concepts of Statistical Decision Theory

    1. Front Matter
      Pages 1-1
    2. Michael Zabarankin, Stan Uryasev
      Pages 3-17
    3. Michael Zabarankin, Stan Uryasev
      Pages 19-31
    4. Michael Zabarankin, Stan Uryasev
      Pages 33-41
  3. Statistical Decision Problems

    1. Front Matter
      Pages 41-41
    2. Michael Zabarankin, Stan Uryasev
      Pages 45-52
    3. Michael Zabarankin, Stan Uryasev
      Pages 53-70
    4. Michael Zabarankin, Stan Uryasev
      Pages 71-87
    5. Michael Zabarankin, Stan Uryasev
      Pages 89-99
    6. Michael Zabarankin, Stan Uryasev
      Pages 101-129
  4. Portfolio Safeguard Case Studies

    1. Front Matter
      Pages 131-131
    2. Michael Zabarankin, Stan Uryasev
      Pages 133-240
  5. Back Matter
    Pages 241-249

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.


Portfolio Safeguard software linear regression portfolio portfolio optimization statistical decision making statistical decision problems

Authors and affiliations

  • Michael Zabarankin
    • 1
  • Stan Uryasev
    • 2
  1. 1.Department of Mathematical SciencesStevens Institute of TechnologyHobokenUSA
  2. 2.Department of Industrial and Systems EngUniversity of FloridaGainesvilleUSA

Bibliographic information