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About the Book
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About the Authors
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Ordering Information
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Table of Contents
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Sample Chapter
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Data
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Errata
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Code
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Book Reviews
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Companion R Language Book
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About the Book
Semiparametric regression is concerned with the flexible incorporation of non-linear functional relationships in regression analyses. Any application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software. The book is suitable as a textbook for students with little background in regression as well as a reference book for statistically oriented scientists such as biostatisticians, econometricians, quantitative social scientists, epidemiologists, with a good working knowledge of regression and the desire to begin using more flexible semiparametric models. Even experts on semiparametric regression should find something new here. "Semiparametric Regression is simply lovely. Among statistical books with hard technical content, it is the friendliest I've ever read ..." -- James S. Hodges, Preface of Hodges, J.S. (2014). Richy Parameterized Linear Models. Boca Raton, Florida: CRC Press.: PDF file "This great book ... provides very readable access to semiparametric regression ... and is an inspiring source for new ideas" -- Ludwig Fahrmeir, Biometrics, (2004): PDF file "I found it an easily readable book, its coverage of material was extensive and well-explained ... I found the material useful and recommend it strongly to anyone who is interested in modern nonparametric methods" -- Marian Scott, Journal of the Royal Statistics Society, Series A, (2004): PDF file "There is a lot of material here and it is very sympathetically presented ... I would recommend this book to anyone interested in the field. It is very readable, informative without being heavy and (excellent news) comes in a paperback version" -- Martin Crowder, International Statistical Institute Short Book Reviews, (2004): PDF file "Overall, this is a superb book" -- Thomas P. Ryan, Journal of Quality Technology, (2005): PDF file "This book in an excellent addition to the growing literature on smoothing... The authors ... have written a clear and concise text" -- Esteban Walker, Technometrics, (2005): PDF file "This is a book that I would strongly recommend to practitioners who want to learn nonparametric regression techniques and apply them to their own problems" -- Yoonkyung Lee, Journal of the American Statistical Association, (2006): PDF file "Another great book by the team of Ruppert and Carroll" -- Michael R. Chernick, Amazon.com, (2008): PDF file |
