03579nam 22005895 450 991030345250332120250610110407.09781493988532149398853010.1007/978-1-4939-8853-2(CKB)4100000007204971(MiAaPQ)EBC5614862(DE-He213)978-1-4939-8853-2(PPN)232961212(MiAaPQ)EBC29092407(EXLCZ)99410000000720497120181212d2018 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierSemiparametric Regression with R /by Jaroslaw Harezlak, David Ruppert, Matt P. Wand1st ed. 2018.New York, NY :Springer New York :Imprint: Springer,2018.1 online resource (341 pages)Use R!,2197-57449781493988518 1493988514 Introduction -- Penalized Splines -- Generalized Additive Models -- Semiparametric Regression Analysis of Grouped Data -- Bivariate Function Extensions -- Selection of Additional Topics.-Index.This 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.Use R!,2197-5744StatisticsBiometryStatisticsStatistical Theory and MethodsBiostatisticsStatistics in Business, Management, Economics, Finance, InsuranceStatistics.Biometry.Statistics.Statistical Theory and Methods.Biostatistics.Statistics in Business, Management, Economics, Finance, Insurance.519.536Harezlak Jaroslawauthttp://id.loc.gov/vocabulary/relators/aut767954Ruppert Davidauthttp://id.loc.gov/vocabulary/relators/autWand Matt Pauthttp://id.loc.gov/vocabulary/relators/autBOOK9910303452503321Semiparametric Regression with R1983086UNINA