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010   $a1-4939-8853-0
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(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 $aUse R!,$x2197-5736
311   $a1-4939-8851-4 
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!,$x2197-5736
606   $aStatistics 
606   $aR (Computer program language)
606   $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001
606   $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030
606   $aStatistics for Business, Management, Economics, Finance, Insurance$3https://scigraph.springernature.com/ontologies/product-market-codes/S17010
615  0$aStatistics .
615  0$aR (Computer program language)
615 14$aStatistical Theory and Methods.
615 24$aStatistics for Life Sciences, Medicine, Health Sciences.
615 24$aStatistics for 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