LEADER 03384oam 2200481 450 001 9910299961503321 005 20190911103512.0 010 $a1-4614-8471-5 024 7 $a10.1007/978-1-4614-8471-4 035 $a(OCoLC)869558203 035 $a(MiFhGG)GVRL6YTB 035 $a(EXLCZ)993710000000078711 100 $a20130801d2014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical decision problems $eselected concepts and portfolio safeguard case studies /$fMichael Zabarankin, Stan Uryasev 205 $a1st ed. 2014. 210 1$aNew York :$cSpringer,$d2014. 215 $a1 online resource (xiv, 249 pages) $cillustrations 225 1 $aSpringer Optimization and Its Applications,$x1931-6828 ;$v85 300 $a"ISSN: 1931-6828." 311 $a1-4614-8470-7 320 $aIncludes bibliographical references and index. 327 $a1. 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. 330 $aStatistical Decision Problems presents a quick and concise introduction into the theory of risk, deviation and error measures that play a key role in statistical decision problems. It introduces state-of-the-art practical decision making through twenty-one case studies from real-life applications. The case studies cover a broad area of topics and the authors include links with source code and data, a very helpful tool for the reader. In its core, the text demonstrates how to use different factors to formulate statistical decision problems arising in various risk management applications, such as optimal hedging, portfolio optimization, cash flow matching, classification, and more.   The presentation is organized into three parts: selected concepts of statistical decision theory, statistical decision problems, and case studies with portfolio safeguard. The text is primarily aimed at practitioners in the areas of risk management, decision making, and statistics. However, the inclusion of a fair bit of mathematical rigor renders this monograph an excellent introduction to the theory of general error, deviation, and risk measures for graduate students. It can be used as supplementary reading for graduate courses including statistical analysis, data mining, stochastic programming, financial engineering, to name a few. The high level of detail may serve useful to applied mathematicians, engineers, and statisticians interested in modeling and managing risk in various applications. 410 0$aSpringer optimization and its applications ;$vvolume 85. 606 $aStatistical decision$vCase studies 606 $aMathematical optimization 615 0$aStatistical decision 615 0$aMathematical optimization. 676 $a519.542 686 $aQH 233$2rvk 700 $aZabarankin$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut$0721738 702 $aUriasev$b S. P$g(Stanislav Pavlovich), 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910299961503321 996 $aStatistical Decision Problems$92540395 997 $aUNINA