000 03835nam a22005655i 4500
001 978-1-4614-8471-4
003 DE-He213
005 20140220082501.0
007 cr nn 008mamaa
008 131216s2014 xxu| s |||| 0|eng d
020 _a9781461484714
_9978-1-4614-8471-4
024 7 _a10.1007/978-1-4614-8471-4
_2doi
050 4 _aQA402-402.37
050 4 _aT57.6-57.97
072 7 _aKJT
_2bicssc
072 7 _aKJM
_2bicssc
072 7 _aBUS049000
_2bisacsh
072 7 _aBUS042000
_2bisacsh
082 0 4 _a519.6
_223
100 1 _aZabarankin, Michael.
_eauthor.
245 1 0 _aStatistical Decision Problems
_h[electronic resource] :
_bSelected Concepts and Portfolio Safeguard Case Studies /
_cby Michael Zabarankin, Stan Uryasev.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXIV, 249 p. 9 illus., 4 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Optimization and Its Applications,
_x1931-6828 ;
_v85
505 0 _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.
520 _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.
650 0 _aMathematics.
650 0 _aData mining.
650 0 _aMathematical optimization.
650 0 _aDistribution (Probability theory).
650 0 _aOperations research.
650 1 4 _aMathematics.
650 2 4 _aOperations Research, Management Science.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aOptimization.
650 2 4 _aOperation Research/Decision Theory.
700 1 _aUryasev, Stan.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461484707
830 0 _aSpringer Optimization and Its Applications,
_x1931-6828 ;
_v85
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-8471-4
912 _aZDB-2-SMA
999 _c92178
_d92178