Generalized Additive Models (2nd Ed.) An Introduction with R, Second Edition Chapman & Hall/CRC Texts in Statistical Science Series
Auteur : Wood Simon N.
The first edition of this book has established itself as one of the leading references on generalized additive models (GAMs), and the only book on the topic to be introductory in nature with a wealth of practical examples and software implementation. It is self-contained, providing the necessary background in linear models, linear mixed models, and generalized linear models (GLMs), before presenting a balanced treatment of the theory and applications of GAMs and related models.
The author bases his approach on a framework of penalized regression splines, and while firmly focused on the practical aspects of GAMs, discussions include fairly full explanations of the theory underlying the methods. Use of R software helps explain the theory and illustrates the practical application of the methodology. Each chapter contains an extensive set of exercises, with solutions in an appendix or in the book?s R data package gamair, to enable use as a course text or for self-study.
Preface
Linear Models
Linear Mixed Models
Generalized Linear Models
Introducing GAMs
Smoothers
GAM theory
GAMs in Practice: mgcv
Appendices A,B,C
Date de parution : 06-2017
Ouvrage de 476 p.
15.6x23.4 cm
Thème de Generalized Additive Models :
Mots-clés :
Thin Plate Regression Spline; smoothing; Smoothing Parameter; generalized linear models; AIC Comparison; mixed models; Linear Mixed Model; random effects; Regression Spline; splines; GCV Score; likelihood inference; Co-variance Matrix; Penalized Regression Spline; Soap Film; GLR Testing; Large Sample Limit; Deviance Residuals; QR Decomposition; Single Index Model; Smoothing Parameter Estimation; Ordinary Linear Model; Ck Level; Linear Predictor; Piecewise Linear; Penalty Matrix; Cubic Spline; Model Manifold; Gam; Equal Fit; Smooth Terms