Semiparametric Regression with R

  • Jaroslaw Harezlak
  • David Ruppert
  • Matt P. Wand

Part of the Use R! book series (USE R)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Jaroslaw Harezlak, David Ruppert, Matt P. Wand
    Pages 1-14
  3. Jaroslaw Harezlak, David Ruppert, Matt P. Wand
    Pages 15-70
  4. Jaroslaw Harezlak, David Ruppert, Matt P. Wand
    Pages 71-128
  5. Jaroslaw Harezlak, David Ruppert, Matt P. Wand
    Pages 129-172
  6. Jaroslaw Harezlak, David Ruppert, Matt P. Wand
    Pages 173-220
  7. Jaroslaw Harezlak, David Ruppert, Matt P. Wand
    Pages 221-314
  8. Back Matter
    Pages 315-331

About this book


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.


semiparametric regression analysis regression analysis bivariate function extensions generalized additive models penalized spines Bayesian semiparametric regression

Authors and affiliations

  • Jaroslaw Harezlak
    • 1
  • David Ruppert
    • 2
  • Matt P. Wand
    • 3
  1. 1.School of Public HealthIndiana University BloomingtonBloomingtonUSA
  2. 2.Department of Statistical ScienceCornell UniversityIthacaUSA
  3. 3.School of Mathematical and Physical SciencesUniversity of Technology SydneyUltimoAustralia

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