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## Auteur Faraway, J.J. |

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Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models / Faraway, J.J. (2006)

Titre : Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models Type de document : livre Auteurs : Faraway, J.J., Auteur Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2006 Collection : Texts in statistical science Importance : 301 p. ISBN/ISSN/EAN : 978-1-58488-424-8 Langues : Anglais ( eng)Mots-clÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site (www.stat.lsa.umich.edu/~faraway/ELM) holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught. En ligne : http://books.google.fr/books?id=ODcRsWpGji4C&dq=extending+the+linear+model+with+ [...] Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models [livre] / Faraway, J.J., Auteur . - Boca Raton : Chapman & Hall/CRC, 2006 . - 301 p.. - (Texts in statistical science) .ISBN: 978-1-58488-424-8

Langues : Anglais (eng)

Mots-clÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Linear models are central to the practice of statistics and form the foundation of a vast range of statistical methodologies. Julian J. Faraway's critically acclaimed Linear Models with R examined regression and analysis of variance, demonstrated the different methods available, and showed in which situations each one applies. Following in those footsteps, Extending the Linear Model with R surveys the techniques that grow from the regression model, presenting three extensions to that framework: generalized linear models (GLMs), mixed effect models, and nonparametric regression models. The author's treatment is thoroughly modern and covers topics that include GLM diagnostics, generalized linear mixed models, trees, and even the use of neural networks in statistics. To demonstrate the interplay of theory and practice, throughout the book the author weaves the use of the R software environment to analyze the data of real examples, providing all of the R commands necessary to reproduce the analyses. A supporting Web site (www.stat.lsa.umich.edu/~faraway/ELM) holds all of the data described in the book. Statisticians need to be familiar with a broad range of ideas and techniques. This book provides a well-stocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduate-level courses in this area are taught. En ligne : http://books.google.fr/books?id=ODcRsWpGji4C&dq=extending+the+linear+model+with+ [...] ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 68106 FAR_11_68106 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043

Titre : Generalized additive models: an introduction with R Type de document : livre Auteurs : Faraway, J.J. Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2006 Collection : Texts in statistical science, 67 Importance : 392 p. ISBN/ISSN/EAN : 978-1-58488-474-3 Langues : Anglais ( eng)Mots-clÃ©s : R (Computer program language) Statistical methods RÃ©sumÃ© : Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. 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. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling. En ligne : http://www.crcpress.co.uk/shopping_cart/products/product_detail.asp?sku=C4746 Generalized additive models: an introduction with R [livre] / Faraway, J.J. . - Boca Raton : Chapman & Hall/CRC, 2006 . - 392 p.. - (Texts in statistical science, 67) .ISBN: 978-1-58488-474-3

Langues : Anglais (eng)

Mots-clÃ©s : R (Computer program language) Statistical methods RÃ©sumÃ© : Now in widespread use, generalized additive models (GAMs) have evolved into a standard statistical methodology of considerable flexibility. While Hastie and Tibshirani's outstanding 1990 research monograph on GAMs is largely responsible for this, there has been a long-standing need for an accessible introductory treatment of the subject that also emphasizes recent penalized regression spline approaches to GAMs and the mixed model extensions of these models. 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. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially self-contained, Generalized Additive Models: An Introduction with R prepares readers with the practical skills and the theoretical background needed to use and understand GAMs and to move on to other GAM-related methods and models, such as SS-ANOVA, P-splines, backfitting and Bayesian approaches to smoothing and additive modelling. En ligne : http://www.crcpress.co.uk/shopping_cart/products/product_detail.asp?sku=C4746 ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 68131 FAR_11_68131 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043

Titre : Linear models with R Type de document : livre Auteurs : Faraway, J.J. Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2005 Collection : Texts in statistical science Importance : 229 p. ISBN/ISSN/EAN : 978-1-58488-425-5 Langues : Anglais ( eng)Mots-clÃ©s : Analysis of variance Linear models Mathematical models R (Computer program language) Regression analysis RÃ©sumÃ© : Books on regression and the analysis of variance abound-many are introductory, many are theoretical. While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from as well, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R. In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http://www.stat.lsa.umich.edu/~faraway/LMR/. The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it. [RÃ©sumÃ© Ã©diteur] En ligne : http://books.google.fr/books?id=fvenzpofkagC&dq=linear+models+with+r&pg=PP1&ots= [...] Linear models with R [livre] / Faraway, J.J. . - Boca Raton : Chapman & Hall/CRC, 2005 . - 229 p.. - (Texts in statistical science) .ISBN: 978-1-58488-425-5

Langues : Anglais (eng)

Mots-clÃ©s : RÃ©sumÃ© : Books on regression and the analysis of variance abound-many are introductory, many are theoretical. While most of them do serve a purpose, the fact remains that data analysis cannot be properly learned without actually doing it, and this means using a statistical software package. There are many of these to choose from as well, all with their particular strengths and weaknesses. Lately, however, one such package has begun to rise above the others thanks to its free availability, its versatility as a programming language, and its interactivity. That software is R. In the first book that directly uses R to teach data analysis, Linear Models with R focuses on the practice of regression and analysis of variance. It clearly demonstrates the different methods available and more importantly, in which situations each one applies. It covers all of the standard topics, from the basics of estimation to missing data, factorial designs, and block designs, but it also includes discussion on topics, such as model uncertainty, rarely addressed in books of this type. The presentation incorporates an abundance of examples that clarify both the use of each technique and the conclusions one can draw from the results. All of the data sets used in the book are available for download from http://www.stat.lsa.umich.edu/~faraway/LMR/. The author assumes that readers know the essentials of statistical inference and have a basic knowledge of data analysis, linear algebra, and calculus. The treatment reflects his view of statistical theory and his belief that qualitative statistical concepts, while somewhat more difficult to learn, are just as important because they enable us to practice statistics rather than just talk about it. [RÃ©sumÃ© Ã©diteur] En ligne : http://books.google.fr/books?id=fvenzpofkagC&dq=linear+models+with+r&pg=PP1&ots= [...] ## Exemplaires (1)

Code-barres Cote Support Localisation Section DisponibilitÃ© 68137 FAR_11_68137 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043