Semiparametric Regression with R
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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York, NY :
Springer New York ,
2018.
|
| Edition: | 1st ed. 2018. |
| Series: | Use R!,
|
| Subjects: | |
| ISBN: | 9781493988532 |
| ISSN: | 2197-5736 |
| Online Access: |
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| Item Description: | Mathematics and Statistics |
| Physical Description: | XI, 331 p. 144 illus., 142 illus. in color. online resource. |
| ISBN: | 9781493988532 |
| ISSN: | 2197-5736 |