LEADER 04192nam 22007815 450 001 9910736996503321 005 20251008131351.0 010 $a9783031333903 010 $a303133390X 024 7 $a10.1007/978-3-031-33390-3 035 $a(CKB)27926266400041 035 $a(DE-He213)978-3-031-33390-3 035 $a(MiAaPQ)EBC30766894 035 $a(Au-PeEL)EBL30766894 035 $a(PPN)272261114 035 $a(MiAaPQ)EBC30766893 035 $a(Au-PeEL)EBL30766893 035 $a(MiAaPQ)EBC30673883 035 $a(Au-PeEL)EBL30673883 035 $a(OCoLC)1392163422 035 $a(EXLCZ)9927926266400041 100 $a20230802d2023 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Statistical Learning $eWith Case Studies in Stata /$fby Matthias Schonlau 205 $a1st ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource 225 1 $aStatistics and Computing,$x2197-1706 311 08$a9783031333897 320 $aIncludes bibliographical references and index. 327 $aPreface -- 1 Prologue -- 2 Statistical Learning: Concepts -- 3 Statistical Learning: Practical Aspects -- 4 Logistic Regression -- 5 Lasso and Friends -- 6 Working with Text Data -- 7 Nearest Neighbors -- 8 The Naive Bayes Classifier -- 9 Trees -- 10 Random Forests -- 11 Boosting -- 12 Support Vector Machines -- 13 Feature Engineering -- 14 Neural Networks -- 15 Stacking -- Index. 330 $aThis textbook provides an accessible overview of statistical learning methods and techniques, and includes case studies using the statistical software Stata. After introductory material on statistical learning concepts and practical aspects, each further chapter is devoted to a statistical learning algorithm or a group of related techniques. In particular, the book presents logistic regression, regularized linear models such as the Lasso, nearest neighbors, the Naive Bayes classifier, classification trees, random forests, boosting, support vector machines, feature engineering, neural networks, and stacking. It also explains how to construct n-gram variables from text data. Examples, conceptual exercises and exercises using software are featured throughout, together with case studies in Stata, mostly from the social sciences; true to the book?s goal to facilitate the use of modern methods of data science in the field. Although mainly intended for upper undergraduate and graduate students in the social sciences, given its applied nature, the book will equally appeal to readers from other disciplines, including the health sciences, statistics, engineering and computer science. 410 0$aStatistics and Computing,$x2197-1706 606 $aMachine learning 606 $aSocial sciences$xStatistical methods 606 $aStatistics 606 $aStatistics$xComputer programs 606 $aQuantitative research 606 $aStatistical Learning 606 $aMachine Learning 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aStatistical Software 606 $aData Analysis and Big Data 615 0$aMachine learning. 615 0$aSocial sciences$xStatistical methods. 615 0$aStatistics. 615 0$aStatistics$xComputer programs. 615 0$aQuantitative research. 615 14$aStatistical Learning. 615 24$aMachine Learning. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aStatistical Software. 615 24$aData Analysis and Big Data. 676 $a006.31 700 $aSchonlau$b Matthias$01741902 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910736996503321 996 $aApplied Statistical Learning$94168286 997 $aUNINA