About the Book
Semiparametric regression extends parametric regression by allowing smooth nonlinear 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 userfriendly 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.
"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).
"I also very much like that it's packed with visualizations and largely based on
worked examples with real data and backed by working code."  Bob Carpenter, Statistical Modeling, Causal Inference,
and Social Science (Columbia University, U.S.A.)
blog entry (6th May 2019)
