LEADER 03579nam 22005895 450 001 9910303452503321 005 20250610110407.0 010 $a9781493988532 010 $a1493988530 024 7 $a10.1007/978-1-4939-8853-2 035 $a(CKB)4100000007204971 035 $a(MiAaPQ)EBC5614862 035 $a(DE-He213)978-1-4939-8853-2 035 $a(PPN)232961212 035 $a(MiAaPQ)EBC29092407 035 $a(EXLCZ)994100000007204971 100 $a20181212d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSemiparametric Regression with R /$fby Jaroslaw Harezlak, David Ruppert, Matt P. Wand 205 $a1st ed. 2018. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2018. 215 $a1 online resource (341 pages) 225 1 $aUse R!,$x2197-5744 311 08$a9781493988518 311 08$a1493988514 327 $aIntroduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-Index. 330 $aThis easy-to-follow applied book expands upon the authors? prior work on semiparametric regression to include the use of R software. In 2003, authors Ruppert and Wand co-wrote Semiparametric Regression with R.J. Carroll, which introduced the techniques and benefits of semiparametric regression in a concise and user-friendly fashion. Fifteen years later, semiparametric regression is applied widely, powerful new methodology is continually being developed, and advances in the R computing environment make it easier than ever before to carry out analyses. Semiparametric Regression with R introduces the basic concepts of semiparametric regression with a focus on applications and R software. This volume features case studies from environmental, economic, financial, and other fields. The examples and corresponding code can be used or adapted to apply semiparametric regression to a wide range of problems. It contains more than fifty exercises, and the accompanying HRW package contains all datasets and scripts used in the book, as well as some useful R functions. This book is suitable as a textbook for advanced undergraduates and graduate students, as well as a guide for statistically-oriented practitioners, and could be used in conjunction with Semiparametric Regression. Readers are assumed to have a basic knowledge of R and some exposure to linear models. For the underpinning principles, calculus-based probability, statistics, and linear algebra are desirable. 410 0$aUse R!,$x2197-5744 606 $aStatistics 606 $aBiometry 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aBiostatistics 606 $aStatistics in Business, Management, Economics, Finance, Insurance 615 0$aStatistics. 615 0$aBiometry. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aBiostatistics. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 676 $a519.536 700 $aHarezlak$b Jaroslaw$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767954 702 $aRuppert$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aWand$b Matt P$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910303452503321 996 $aSemiparametric Regression with R$91983086 997 $aUNINA