BIBLIOTECA MANUEL BELGRANO - Facultad de Ciencias Económicas - UNC

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Generalized additive models : an introduction with R / Simon N. Wood.

Por: Tipo de material: TextoTextoSeries Texts in statistical scienceDetalles de publicación: Boca Raton, Fl. : Chapman & Hall/CRC, 2006Descripción: xvii, 392 pISBN:
  • 9781584884743
Tema(s): Clasificación CDD:
  • 23 519.282
Recursos en línea:
Contenidos:
1. Linear models -- 2. Generalized linear models -- 3. Introducing GAMs -- 4. Some GAM theory -- 5. GAMs practice: mgcv -- 6. Mixed models and GAMMs -- A: some matrix algebra -- B: solutions to exercises.
Resumen: "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."
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Libro Libro Biblioteca Manuel Belgrano 519.282 W 52985 (Navegar estantería(Abre debajo)) Disponible 52985

Bibliografía: p. 379-383.

1. Linear models -- 2. Generalized linear models -- 3. Introducing GAMs -- 4. Some GAM theory -- 5. GAMs practice: mgcv -- 6. Mixed models and GAMMs -- A: some matrix algebra -- B: solutions to exercises.

"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."

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