Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models / Julian J. Faraway
Tipo de material:
TextoSeries Texts in statistical science ; 66Detalles de publicación: Boca Raton : Chapman and Hall, 2006 Descripción: IX, 301 p. : gráf. ; 24 cmISBN: 1-58488-424-XTema(s): R (Lenguaje de programación) | Modelos matemáticos | Estadística matemática | Análisis de varianza | Análisis de regresiónResumen: This modern statistics text discusses the extension of the linear model through the regression model. It extensively addresses the generalized linear model, GLM diagnostics, generalized linear mixed models, trees, and the use of neural networks in the field of statistics. It discusses the regression model in three forms: through the use of dummy variables for qualitative predictors, by allowing transformation of variables as in the Box-Cox transformation, and the use of weights which allow heterogeneous error structures and the exclusion of outliers. R is used throughout the book to aid with computation and model building.Resumen: Índice: Generalized Linear Model. Binary Data. Poisson Regression. NominalResponse Models. Ordinal Responses. The Gamma GLM. Quasi-Likelihood. Joint Modeling of the Mean and Dispersion. Negative-Binomial. GLM Diagnostics. Random Effects. Repeated Measures.Generalized Linear Mixed Model. Nonparametric Regression. Trees. Multivariate Adaptive regression Splines (MARS). Additive Models. Response Transformation Models. Neural Networks.
| Tipo de ítem | Biblioteca de origen | Signatura | URL | Estado | Fecha de vencimiento | Código de barras | Reserva de ítems |
|---|---|---|---|---|---|---|---|
| Monografías | 02. BIBLIOTECA CAMPUS PUERTO REAL | 519.22/FAR/ext (Navegar estantería(Abre debajo)) | Texto completo | Prestado | 31/01/2017 | 374272693X |
Índice
Bibliografía: p. 289-295
This modern statistics text discusses the extension of the linear model through the regression model. It extensively addresses the generalized linear model, GLM diagnostics, generalized linear mixed models, trees, and the use of neural networks in the field of statistics. It discusses the regression model in three forms: through the use of dummy variables for qualitative predictors, by allowing transformation of variables as in the Box-Cox transformation, and the use of weights which allow heterogeneous error structures and the exclusion of outliers. R is used throughout the book to aid with computation and model building.
Índice: Generalized Linear Model. Binary Data. Poisson Regression. NominalResponse Models. Ordinal Responses. The Gamma GLM. Quasi-Likelihood. Joint Modeling of the Mean and Dispersion. Negative-Binomial. GLM Diagnostics. Random Effects. Repeated Measures.Generalized Linear Mixed Model. Nonparametric Regression. Trees. Multivariate Adaptive regression Splines (MARS). Additive Models. Response Transformation Models. Neural Networks.
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