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Generalized Additive Models: An Introduction with R By Simon Wood


作者: Generalized Additive Models, An Introduction, R By, Simon Wood

日期: 01.01.2011

ISBN: 1584884746

页数: 397

出版社: C[hap]m[an] and H[al]l/C//R\\C 2006

标签: 数学



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Generalized Additive Models An Introduction with R By Simon Wood


Generalized Additive Models: An Introduction with R By Simon Wood

PUBLISHER: C[HAP]M[AN] AND H[AL]L/C//R\\C 2006 | 397 PAGES | ISBN: 1584884746 | PDF |10.55 MB



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 long-standing 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 well-grounded 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 add-on package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix.
Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling.



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