Semiparametric Regression with R [electronic resource] / by Jaroslaw Harezlak, David Ruppert, Matt P. Wand
- Harezlak, Jaroslaw
- New York, NY : Springer New York : Imprint: Springer, 2018.
- Physical Description:
- XI, 331 pages 144 illustrations, 142 illustrations in color : online resource
- Additional Creators:
- Ruppert, David, 1948-, Wand, Matt P., and SpringerLink (Online service)
- Use R!, 2197-5736
- 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.
- Digital File Characteristics:
- text file PDF
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