Extending the linear model with R : generalized linear, mixed effects and nonparametric regression models / Julian J. Faraway

By: Faraway, Julian JMaterial type: TextTextSeries: Texts in statistical science ; 66Publication details: Boca Raton : Chapman and Hall, 2006 Description: IX, 301 p. : gráf. ; 24 cmISBN: 1-58488-424-XSubject(s): R (Lenguaje de programación) | Modelos matemáticos | Estadística matemática | Análisis de varianza | Análisis de regresiónSummary: 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.Summary: 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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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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