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Semiparametric Regression (Cambridge Series in Statistical and Probabilistic Mathematics) Paperback – July 14, 2003

ISBN-13: 978-0521785167 ISBN-10: 0521785162 Edition: 1st

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Product Details

  • Series: Cambridge Series in Statistical and Probabilistic Mathematics (Book 12)
  • Paperback: 404 pages
  • Publisher: Cambridge University Press; 1 edition (July 14, 2003)
  • Language: English
  • ISBN-10: 0521785162
  • ISBN-13: 978-0521785167
  • Product Dimensions: 9.8 x 7.1 x 0.8 inches
  • Shipping Weight: 1.4 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #894,596 in Books (See Top 100 in Books)

Editorial Reviews

Review

"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 as well as hardback."
ISI Short Book Reviews

"This great book is the first one to remove barriers and to close gaps between advanced statistical methodology and applied research in various fields ... I highly recommend this book ... It provides a very readable access to modern semiparametric regression, demonstrates its potential in various applications, and is an inspiring source for new ideas. I enjoyed reading this book."
Biometrics

"... contains clear presentations of new developments in the field and also the state of the art in classical methods... I found it an easily readable book; its coverage of material was extensive and well explained and well illustrated ... I found the material useful and I recommend it strongly to anyone who is interested in modern nonparametric methods, whether they are expert or not ... here are 500-odd pages of good teaching material, nicely done, culminating in the arc-sine law and the Black-Scholes formula: anyone teaching probability would be glad to have it to hand."
Journal of the Royal Statistical Society

"This book provides an extensive overview of techniques for semiparametric regression ... I think it may be very useful for a more practically oriented audience."
Kwantitatieve Methoden

"This book is a very nice book for data analysis and indicates how to flexibly develop and analyze complex models using penalized spline functions. The examples are nontrivial and very useful."
Mathematical Reviews

"Although appealing to statistically-oriented scientists, this book also should not fail to attract the attention of experts in the field, because it provides a fresh perspective on smoothing and addresses ongoing computational and theoretical issues. The text is quite comprehensive, and the chapters are carefully organized for a coherent development of the subject...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 without being burdened by advanced mathematical concepts such as a reproducing kernel Hilbert space."
Yoonkyung Lee, Journal of the American Statistical Association

Book Description

Science abounds with problems where the data are noisy and the answer is not a straight line. Semiparametric regression aims to make sense of such data. Application areas include engineering, finance, medicine and public health. Semiparametric Regression Modeling explains this topic in a concise and modular fashion. The book is pitched towarards researchers and pro fessionals with little background in regression and 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.

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Most Helpful Customer Reviews

39 of 39 people found the following review helpful By Michael R. Chernick on January 20, 2008
Format: Paperback
David Ruppert and Ray Carroll have been a research team for over 25 years. They have published many articles and books on regression analysis. These articles are always very clearly written and are great at showing the big picture and not just the nitty gritty details of the theorems that they prove. Two of my favorite books that they published are "Transformations and Weighting in Regression" published by Chapman & Hall in 1988 and "Measurement Error in Nonlinear Models " with Stefanski in 1995 and also published by Chapman & Hall.

This book is no exception. It is lucid in expostion and paints a general picture summarizing the area of nonparametric regression models and incorporating them with parametric regression both linear and nonlinear.

Their work has also been motivated by the desire to extend the theory of regression models to practical problems where the standard theory with assumptions such as linearity, normality, and homogeneity of variance don't hold.

In the first chapter, they motivate their methods through a number of examples in the areas of health science and environmental pollution problems. Chapter two goes through the standard linear regression models and the diagnostic checks for those models. They also cover other practical issues including model selection, use of transformations and extensions to nonlinear models. The special case of polynomial regression (a particular example of linear regression) is presented in detail.

Chapter 3 on scatterplot smoothing introduces many of the key ideas to their approach to semiparametric regression. Their approach in its most general form is based on mixed models which are introduced in chapter 4.
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