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＜電子ブック＞
Semiparametric Regression with R / by Jaroslaw Harezlak, David Ruppert, Matt P. Wand
(Use R!. ISSN:21975744)

版	1st ed. 2018.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2018
大きさ	XI, 331 p. 144 illus., 142 illus. in color : online resource
著者標目	*Harezlak, Jaroslaw author Ruppert, David author Wand, Matt P author SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Statistics in Business, Management, Economics, Finance, Insurance
一般注記	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 HTTP:URL=https://doi.org/10.1007/978-1-4939-8853-2

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