About the Book

  Semiparametric Regression with R

   Jaroslaw Harezlak, David Ruppert and Matt P. Wand

   December 2018
   xi+331 pages
   144 color figures
   ISBN: 978-1-4939-8851-8
   ISBN: 978-1-4939-8853-2 (eBook)


All of the examples and
exercises in this book
depend on the R comp-
uting environment.
However, since R is
continually changing
readers should regularly
check the book's:

News, Software Updates and
Errata web-site

Semiparametric regression extends parametric regression by allowing smooth non-linear predictor effects. In 2003, two of the authors and R.J. Carroll published the book Semiparametric Regression which introduced the techniques and benefits of semiparametric regression in a concise and user-friendly fashion. Fifteen years later, semiparametric regression is being applied in numerous areas of application, powerful new methodology is continually being developed and advances in the R computing environment are making it easier than ever before to carry out analyses. Semiparametric Regression with R introduces the basic concepts of semiparametric regression and is focused on applications and the use of R software. Case studies are taken from environmental, economic, financial, medical and other areas of applications. The book contains more than 50 exercises. The HRW package that accompanies the book contains all of the scripts used in the book, as well as datasets and functions.

"... a truly heroic book!" -- Zixiao Wang, Yi Feng and Lin Liu, Journal of the American Statistical Association (2022).

"The book is very well written and produced." "Overall, this is a very impressive work." -- Charles E. Heckler, Technometrics (2022).

"The authors have done tremendously valuable work by presenting the computational details for implementation of the methods developed in this vast area." -- Tapio Nummi, International Statistical Review (2020).

© J. Harezlak, D. Ruppert and M.P. Wand 2018