Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series) [Hardcover]

Adrian W Bowman (Author), Adelchi Azzalini (Author)

Book Description

Publication Date: November 13, 1997 | ISBN-10: 0198523963 | ISBN-13: 978-0198523963

This book describes the use of smoothing techniques in statistics and includes both density estimation and nonparametric regression. Incorporating recent advances, it describes a variety of ways to apply these methods to practical problems. Although the emphasis is on using smoothing techniques to explore data graphically, the discussion also covers data analysis with nonparametric curves, as an extension of more standard parametric models. Intended as an introduction, with a focus on applications rather than on detailed theory, the book will be equally valuable for undergraduate and graduate students in statistics and for a wide range of scientists interested in statistical techniques.

The text makes extensive reference to S-Plus, a powerful computing environment for exploring data, and provides many S-Plus functions and example scripts. This material, however, is independent of the main body of text and may be skipped by readers not interested in S-Plus.

Editorial Reviews

Review

"An up-to-date book with the most recent state of the art. . . . Accessible to nonmathematical readers. . .There is a rich choice of examples, exercises, hints for further reading and S-Plus illustrations." --N. Veraverbeke, Limburgs Universitair Centrum, Diepenbeek, Belgium

"[T]his book provides an overview of smoothing techniques used in data analysis, with emphasis on one- and two-dimensional data. The authors' aim is to complement the existing books by focusing on intuitive presentation of the ideas and on practical issues of inference rather than estimation. The book consists of eight chapters and 193 pages, with the first two chapters devoted to density estimation and the last six . . . concentrating on smoothing in regression and time series. Real data are used throughout to illustrate the techniques. . . . [T]he book attempts to be both a practical introduction to smoothing and an outline of the methodological and theoretical development of the subject. It does reasonably well at both, but its strength is in showing the techniques and illustrating them on datasets. I think it will be a quite useful book for a research or applied statistician wanting an overview of the subject with examples and references."--Technometrics

"This instructive textbook provides an excellent introduction to smoothing, with an emphasis on methods, applications on real data, and subsequent inferences. If you are an applied and/or a quantitatively oriented researcher who is unfamiliar with (or suspicious of) smoothing methods, you will definitely appreciate the book's level and practical focus, as the authors have presented the methodology and have demonstrated implementation clearly on real datasets with descriptive interpretations of the results. . . . This book would serve as an excellent textbook for a masters-level course on smoothing because it focuses on actual practice, through real datasets and corresponding software (available on-line as described in Appendix A) and because of the instructive exercises that conclude each chapter."--Journal of the American Statistical Association

About the Author

Professor Adrian Bowman, Department of Statistics, University of Glasgow, Glasgow, G12 8QQ, Scotland, U.K. Tel: [phone], Fax: [phone], E-mail: [e-mail] Professor Adelchi Azzalini, Department of Statistical Sciences, University of Padova, Via S.Francesco 33, 35121 Padova, Italy Tel:[phone], Fax: [phone], E-mail: [e-mail]

Product Details

• Hardcover: 208 pages
• Publisher: Oxford University Press, USA (November 13, 1997)
• Language: English
• ISBN-10: 0198523963
• ISBN-13: 978-0198523963
• Product Dimensions: 9.2 x 6.1 x 0.7 inches
• Shipping Weight: 1 pounds (View shipping rates and policies)
• Average Customer Review: 4.5 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #825,942 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

25 of 25 people found the following review helpful:

4.0 out of 5 stars important modern topic in statistics, February 9, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series) (Hardcover)

This book covers parametric and nonparametric regression, logistic regression, density estimation and smoothing, semiparametric and additive modeling. The underlying approach is to use kernel methods for smoothing and density estimation. They point to bootstrap methods as a way to put confidence bands on the density estimates. SPlus software is used to illustrate the techniques. The exposition is clear and concise and coverage is fairly broad. Interesting topics such as time series, longitudinal data analysis comparing regression curves and exploratory methods are all covered.

15 of 15 people found the following review helpful:

5.0 out of 5 stars Excellent book on kernel smoothing, May 28, 2001

By 

Roger Peng (Baltimore, MD USA) - See all my reviews
(REAL NAME)   

This review is from: Applied Smoothing Techniques for Data Analysis: The Kernel Approach with S-Plus Illustrations (Oxford Statistical Science Series) (Hardcover)

This book is probably my #1 reference for kernel smoothing. It covers enough theory (bias and variance calculations) without getting stuck in mathematical details. Also the code samples are very useful and are carefully interspersed throughout the text. I think this is a great book for the applied statistician who wants to get up and running with kernel smoothing methods.