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"That the authors succeed in conveying their message is largely due to the extremely clear and effective organization of the text. General concepts are introduced, illustrated with relatively simple applications, and then re-examined at a more theoretical level." -Journal of the Royal Statistical Society, Series C "This book aims to 'provide an up-to-date survey of current research in additive modelling, ' with the 'emphasis on practical rather than theoretical.' With that in mind, the book does extremely well." -Statistics in Medicine "Generalized Additive Models is a good starting point for researchers wishing to initiate themselves in the vast and burgeoning area of large-sample nonparametric model fitting." -Journal of the American Statistical Association "Overall, my impressions of this book are very favorable, and I consider it a book worth owning... The material is presented clearly and attractively. I recommend it to anyone interested in or needing to use extensions to the general linear model." -Technometrics ..". a complete introduction to the topic in a single monograph... a very readable book that provides the reader with great insight into a vast array of data analysis techniques... an excellent text for a course on smoothing-based analysis techniques... highly recommended to any reader interested in an introduction to many of the recent developments in data analysis." -Siam Review
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This book describes an array of power tools for data analysis that are based on nonparametric regression and smoothing techniques. These methods relax the linear assumption of many standard models and allow analysts to uncover structure in the data that might otherwise have been missed. While McCullagh and Nelder's Generalized Linear Models shows how to extend the usual linear methodology to cover analysis of a range of data types, Generalized Additive Models enhances this methodology even further by incorporating the flexibility of nonparametric regression. Clear prose, exercises in each chapter, and case studies enhance this popular text.
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One of the most popular and useful tools in data analysis is the linear regression model. Read the first page
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
One of the most popular and useful tools in data analysis is the linear regression model. Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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5.0 out of 5 stars (3 customer reviews)
30 of 31 people found the following review helpful:
5.0 out of 5 stars
nice generalization of linear models, 7 Feb 2008
By Michael R. Chernick "statman31147" - Published on Amazon.com
This review is from: Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
Believe it or not this important topic in applied statistics was developed by Hastie and Tibshirani when they were graduate students, a sure sign of greatness to come. After their graduation this highly acclaimed book came out explaining both the theory and pratice of generalized additive models in a clear and concise way. Generalized additive models are similar to generalized linear models in their additive structure but the form of the additive functions is much more general.
Both Hastie and Tibshirani are now Stanford professors in the Statistics Department and both have written other excellent books including their joint publication with Jerry Friedman "The Elements of Statistical Learning" and Tibshirani along with Efron wrote an excellent monograph on bootstrap.
15 of 15 people found the following review helpful:
5.0 out of 5 stars
A complex topic made readable., 14 April 2000
By A Customer - Published on Amazon.com
This review is from: Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
A medical doctor recommended that I (a statistician) use generalized additive models on a project that he was involved with. We were pleased with the results of the modelling as they gave us some new insights on his topic and it was very rewardingBecause we were so impressed with the results, we both took the above book out of the library to read. I started the book with some trepidation as I feared it was going to be a complex topic and in some respects I was right. However, right from the encouraging quotes in the preface and into the text itself, I felt the authors were making a great effort to make the book readable.
The medico found that the explanations and examples were well written. His comment was that even a non-statistician could grasp what was going on but that there was enough theory that a statistician would be happy too!
5.0 out of 5 stars
Seminal work in modern linear models, 9 Feb 2011
By Chad R. Bhatti - Published on Amazon.com
This review is from: Generalized Additive Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) (Hardcover)
Generalized Additive Models (GAMs) represent the modern and computational extension of Generalized Linear Models (GLMs) (see McCullagh and Nelder) which allow for nonlinear effects in the predictor variables. These nonlinear effects are specified nonparametrically through estimation using a scatterplot smoother. GAMs are frequently used in industry (e.g. FICO scores) due to their specification flexibility and ability to provide a means of dimension reduction. This book provides an introduction to Generalized Additive Models straight from the source. There is not really another book on the subject worth considering.
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