LEADER 04333oam 2200673I 450 001 9910790030203321 005 20230801221712.0 010 $a0-429-10735-8 010 $a1-283-59682-2 010 $a9786613909275 010 $a1-4398-6948-0 024 7 $a10.1201/b11639 035 $a(CKB)2670000000151658 035 $a(EBL)870690 035 $a(OCoLC)778497234 035 $a(SSID)ssj0000611903 035 $a(PQKBManifestationID)11362718 035 $a(PQKBTitleCode)TC0000611903 035 $a(PQKBWorkID)10666578 035 $a(PQKB)10556196 035 $a(MiAaPQ)EBC870690 035 $a(Au-PeEL)EBL870690 035 $a(CaPaEBR)ebr10535478 035 $a(CaONFJC)MIL390927 035 $a(EXLCZ)992670000000151658 100 $a20180706d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aFoundations of predictive analytics /$fJames Wu, Stephen Coggeshall 210 1$aBoca Raton, Fla. :$cCRC Press,$d2012. 215 $a1 online resource (335 p.) 225 1 $aChapman & Hall/CRC data mining and knowledge discovery series 300 $a"A Chapman & Hall book." 311 $a1-4665-3881-3 311 $a1-4398-6946-4 320 $aIncludes bibliographical references. 327 $aFront Cover; Contents; List of Figures; List of Tables; Preface; 1. Introduction; 2. Properties of Statistical Distributions; 3. Important Matrix Relationships; 4. Linear Modeling and Regression; 5. Nonlinear Modeling; 6. Time Series Analysis; 7. Data Preparation and Variable Selection; 8. Model Goodness Measures; 9. Optimization Methods; 10. Miscellaneous Topics; Appendix A: Useful Mathematical Relations; Appendix B: DataMinerXL - Microsoft Excel Add-In for Building Predictive Models; Bibliography 330 $a"Preface this text is a summary of techniques of data analysis and modeling that the authors have encountered and used in our two-decades experience of practicing the art of applied data mining across many different fields. The authors have worked in this field together and separately in many large and small companies, including the Los Alamos National Laboratory, Bank One (JPMorgan Chase), Morgan Stanley, and the startups of the Center for Adaptive Systems Applications (CASA), the Los Alamos Computational Group and ID Analytics. We have applied these techniques to traditional and nontraditional problems in a wide range of areas including consumer behavior modeling (credit, fraud, marketing), consumer products, stock forecasting, fund analysis, asset allocation, and equity and xed income options pricing. This monograph provides the necessary information for understanding the common techniques for exploratory data analysis and modeling. It also explains the details of the algorithms behind these techniques, including underlying assumptions and mathematical formulations. It is the authors' opinion that in order to apply di erent techniques to di erent problems appropriately, it is essential to understand the assumptions and theory behind each technique. It is recognized that this work is far from a complete treatise on the subject. Many excellent additional texts exist on the popular subjects and it was not a goal for this present text to be a complete compilation. Rather this text contains various discussions on many practical subjects that are frequently missing from other texts, as well as details on some subjects that are not often or easily found. Thus this text makes an excellent supplemental and referential resource for the practitioners of these subjects"--Provided by publisher. 410 0$aChapman & Hall/CRC data mining and knowledge discovery series. 606 $aData mining 606 $aPredictive control$xMathematical models 606 $aAutomatic control 615 0$aData mining. 615 0$aPredictive control$xMathematical models. 615 0$aAutomatic control. 676 $a006.3/12 686 $aBUS061000$aCOM021030$aCOM037000$2bisacsh 700 $aWu$b James$f1965-$01541742 701 $aCoggeshall$b Stephen$01541743 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790030203321 996 $aFoundations of predictive analytics$93794099 997 $aUNINA