Generalized additive models : an introduction with R / Simon N. Wood.
Tipo de material: TextoSeries Texts in statistical scienceDetalles de publicación: Boca Raton, Fl. : Chapman & Hall/CRC, 2006Descripción: xvii, 392 pISBN:- 9781584884743
- 23 519.282
Tipo de ítem | Biblioteca actual | Signatura topográfica | Estado | Fecha de vencimiento | Código de barras | |
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Libro | Biblioteca Manuel Belgrano | 519.282 W 52985 (Navegar estantería(Abre debajo)) | Disponible | 52985 |
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519.282 H 32624 Monte Carlo experiments with regression involving the box-cox transformation / | 519.282 R 41728 Simulation and the Monte Carlo method / | 519.282 S 54964 Monte Carlo : simulación directa / | 519.282 W 52985 Generalized additive models : an introduction with R / | 519.282 W 56619 Generalized additive models : an introduction with R / | 519.287 A 29021 Matemáticas de la fiabilidaad : fundamentos, prácticas, procedimientos / | 519.287 I 29208 Nociones de fiabilidad / |
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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