Generalized additive models : an introduction with R
 Author/Creator
 Wood, Simon N.
 Language
 English.
 Imprint
 Boca Raton, FL : Chapman & Hall/CRC, 2006.
 Physical description
 xvii, 392 p. : ill. ; 25 cm.
 Series
 Texts in statistical science.
Access
Available online

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QA274.73 .W66 2006

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QA274.73 .W66 2006
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Contents/Summary
 Bibliography
 Includes bibliographical references (p. 379383) and index.
 Contents

 LINEAR MODELS A simple linear model Linear models in general The theory of linear models The geometry of linear modelling Practical linear models Practical modelling with factors General linear model specification in R Further linear modelling theory Exercises GENERALIZED LINEAR MODELS The theory of GLMs Geometry of GLMs GLMs with R Likelihood Exercises INTRODUCING GAMS Introduction Univariate smooth functions Additive models Generalized additive models Summary Exercises SOME GAM THEORY Smoothing bases Setting up GAMs as penalized GLMs Justifying PIRLS Degrees of freedom and residual variance estimation Smoothing Parameter Estimation Criteria Numerical GCV/UBRE: performance iteration Numerical GCV/UBRE optimization by outer iteration Distributional results Confidence interval performance Further GAM theory Other approaches to GAMs Exercises GAMs IN PRACTICE: mgcv Cherry trees again Brain imaging example Air pollution in Chicago example Mackerel egg survey example Portuguese larks example Other packages Exercises MIXED MODELS and GAMMs Mixed models for balanced data Linear mixed models in general Linear mixed models in R Generalized linear mixed models GLMMs with R Generalized additive mixed models GAMMs with R Exercises APPENDICES A Some matrix algebra B Solutions to exercises Bibliography Index.
 (source: Nielsen Book Data)
 Publisher's Summary
 Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a longstanding need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. "Generalized Additive Models: An Introduction with R" imparts a thorough understanding of the theory and practical applications of GAMs and related advanced models, enabling informed use of these very flexible tools. The author bases his approach on a framework of penalized regression splines, and builds a wellgrounded foundation through motivating chapters on linear and generalized linear models. While firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of the freely available R software helps explain the theory and illustrates the practicalities of linear, generalized linear, and generalized additive models, as well as their mixed effect extensions. The treatment is rich with practical examples, and it includes an entire chapter on the analysis of real data sets using R and the author's addon package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially selfcontained, this book prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAMrelated methods and models, such as SSANOVA, Psplines, backfitting and Bayesian approaches to smoothing and additive modelling.
(source: Nielsen Book Data)
Subjects
Bibliographic information
 Publication date
 2006
 Responsibility
 Simon N. Wood.
 Series
 Texts in statistical science
 ISBN
 9781584884743
 1584884746