LEADER 05398nam 22007455 450 001 9910254092303321 005 20200705030110.0 010 $a3-319-44048-9 024 7 $a10.1007/978-3-319-44048-4 035 $a(CKB)3710000000926148 035 $a(DE-He213)978-3-319-44048-4 035 $a(MiAaPQ)EBC6313112 035 $a(MiAaPQ)EBC5578099 035 $a(Au-PeEL)EBL5578099 035 $a(OCoLC)962018223 035 $a(PPN)196323762 035 $a(EXLCZ)993710000000926148 100 $a20161027d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Learning from a Regression Perspective /$fby Richard A. Berk 205 $a2nd ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XXIII, 347 p. 120 illus., 91 illus. in color.) 225 1 $aSpringer Texts in Statistics,$x1431-875X 311 $a3-319-44047-0 327 $aStatistical Learning as a Regression Problem -- Splines, Smoothers, and Kernels -- Classification and Regression Trees (CART) -- Bagging -- Random Forests -- Boosting -- Support Vector Machines -- Some Other Procedures Briefly -- Broader Implications and a Bit of Craft Lore. 330 $aThis textbook considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this can be seen as an extension of nonparametric regression. This fully revised new edition includes important developments over the past 8 years. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis derives from sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. A continued emphasis on the implications for practice runs through the text. Among the statistical learning procedures examined are bagging, random forests, boosting, support vector machines and neural networks. Response variables may be quantitative or categorical. As in the first edition, a unifying theme is supervised learning that can be treated as a form of regression analysis. Key concepts and procedures are illustrated with real applications, especially those with practical implications. A principal instance is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives.  Also provided is helpful craft lore such as not automatically ceding data analysis decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important message is to appreciate the limitation of one?s data and not apply statistical learning procedures that require more than the data can provide. The material is written for upper undergraduate level and graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. The author uses this book in a course on modern regression for the social, behavioral, and biological sciences. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R with code routinely provided. 410 0$aSpringer Texts in Statistics,$x1431-875X 606 $aStatistics  606 $aProbabilities 606 $aPublic health 606 $aPsychology?Methodology 606 $aPsychological measurement 606 $aSocial sciences 606 $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001 606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040 606 $aPublic Health$3https://scigraph.springernature.com/ontologies/product-market-codes/H27002 606 $aPsychological Methods/Evaluation$3https://scigraph.springernature.com/ontologies/product-market-codes/Y20040 606 $aMethodology of the Social Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/X17000 615 0$aStatistics . 615 0$aProbabilities. 615 0$aPublic health. 615 0$aPsychology?Methodology. 615 0$aPsychological measurement. 615 0$aSocial sciences. 615 14$aStatistical Theory and Methods. 615 24$aProbability Theory and Stochastic Processes. 615 24$aStatistics for Social Sciences, Humanities, Law. 615 24$aPublic Health. 615 24$aPsychological Methods/Evaluation. 615 24$aMethodology of the Social Sciences. 676 $a519.2 700 $aBerk$b Richard A$4aut$4http://id.loc.gov/vocabulary/relators/aut$0558720 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254092303321 996 $aStatistical learning from a regression perspective$91523646 997 $aUNINA