Statistical computing with R / Rizzo, M.L. (2008)

Public ISBD Titre : Statistical computing with R Type de document : livre Auteurs : Rizzo, M.L. Editeur : Boca Raton : Chapman & Hall/CRC Année de publication : 2008 Collection : Computer science and data analysis series Importance : 399 p. ISBN/ISSN/EAN : 978-1-58488-545-0 Langues : Anglais (eng) Mots-clés : R (Computer program language)  Statistical methods Résumé : Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions. Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing. [Résumé éditeur] Statistical computing with R [livre] / Rizzo, M.L. . - Boca Raton : Chapman & Hall/CRC, 2008 . - 399 p.. - (Computer science and data analysis series) . ISBN : 978-1-58488-545-0 Langues : Anglais (eng) Mots-clés : R (Computer program language)  Statistical methods Résumé : Computational statistics and statistical computing are two areas that employ computational, graphical, and numerical approaches to solve statistical problems, making the versatile R language an ideal computing environment for these fields. One of the first books on these topics to feature R, Statistical Computing with R covers the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. Suitable for an introductory course in computational statistics or for self-study, it includes R code for all examples and R notes to help explain the R programming concepts. After an overview of computational statistics and an introduction to the R computing environment, the book reviews some basic concepts in probability and classical statistical inference. Each subsequent chapter explores a specific topic in computational statistics. These chapters cover the simulation of random variables from probability distributions, the visualization of multivariate data, Monte Carlo integration and variance reduction methods, Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The final chapter presents a selection of examples that illustrate the application of numerical methods using R functions. Focusing on implementation rather than theory, this text serves as a balanced, accessible introduction to computational statistics and statistical computing. [Résumé éditeur]

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
68239	RIZ_11_68239	Livre	Salle des ouvrages	11_Mathématiques	Sorti jusqu'au 25/05/2043

Analysis of correlated data with SAS and R / Shoukri, M.M. ; Chaudhary, M.A. (2007)

Public ISBD Titre : Analysis of correlated data with SAS and R Type de document : livre Auteurs : Shoukri, M.M. ; Chaudhary, M.A. Mention d'édition : 03 éd. Editeur : Boca Raton : Chapman & Hall/CRC Année de publication : 2007 Importance : 295 p. ISBN/ISSN/EAN : 978-1-58488-619-8 Note générale : Contient un CD-Rom Langues : Anglais (eng) Mots-clés : Epidemiology  Statistical methods Résumé : Previously known as Statistical Methods for Health Sciences, this bestselling resource is one of the first books to discuss the methodologies used for the analysis of clustered and correlated data. While the fundamental objectives of its predecessors remain the same, Analysis of Correlated Data with SAS and R, Third Edition incorporates several additions that take into account recent developments in the field. New to the Third Edition · The introduction of R codes for almost all of the numerous examples solved with SAS · A chapter devoted to the modeling and analyzing of normally distributed variables under clustered sampling designs · A chapter on the analysis of correlated count data that focuses on over-dispersion · Expansion of the analysis of repeated measures and longitudinal data when the response variables are normally distributed · Sample size requirements relevant to the topic being discussed, such as when the data are correlated because the sampling units are physically clustered or because subjects are observed over time · Exercises at the end of each chapter to enhance the understanding of the material covered · An accompanying CD-ROM that contains all the data sets in the book along with the SAS and R codes Assuming a working knowledge of SAS and R, this text provides the necessary concepts and applications for analyzing clustered and correlated data. [Résumé éditeur] Analysis of correlated data with SAS and R [livre] / Shoukri, M.M. ; Chaudhary, M.A.  . -  03 éd. . - Boca Raton : Chapman & Hall/CRC, 2007 . - 295 p. ISBN : 978-1-58488-619-8 Contient un CD-Rom Langues : Anglais (eng) Mots-clés : Epidemiology  Statistical methods Résumé : Previously known as Statistical Methods for Health Sciences, this bestselling resource is one of the first books to discuss the methodologies used for the analysis of clustered and correlated data. While the fundamental objectives of its predecessors remain the same, Analysis of Correlated Data with SAS and R, Third Edition incorporates several additions that take into account recent developments in the field. New to the Third Edition · The introduction of R codes for almost all of the numerous examples solved with SAS · A chapter devoted to the modeling and analyzing of normally distributed variables under clustered sampling designs · A chapter on the analysis of correlated count data that focuses on over-dispersion · Expansion of the analysis of repeated measures and longitudinal data when the response variables are normally distributed · Sample size requirements relevant to the topic being discussed, such as when the data are correlated because the sampling units are physically clustered or because subjects are observed over time · Exercises at the end of each chapter to enhance the understanding of the material covered · An accompanying CD-ROM that contains all the data sets in the book along with the SAS and R codes Assuming a working knowledge of SAS and R, this text provides the necessary concepts and applications for analyzing clustered and correlated data. [Résumé éditeur]

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
68107	SHO_11_68107	Livre	Salle des ouvrages	11_Mathématiques	Sorti jusqu'au 25/05/2043

Statistical methods for spatio-temporal systems / Finkenstädt, B.(Ed.) ; Held, L(Ed.) ; Isham, V.(Ed.) (2007)

Public ISBD Titre : Statistical methods for spatio-temporal systems Type de document : livre Auteurs : Finkenstädt, B.(Ed.) ; Held, L(Ed.) ; Isham, V.(Ed.) Editeur : Boca Raton : Chapman & Hall/CRC Année de publication : 2007 Collection : Monographs on statistics and applied probability, 107 Importance : 286 p. ISBN/ISSN/EAN : 978-1-58488-593-1 Langues : Anglais (eng) Mots-clés : Statistical methods Résumé : Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time covariance functions. The contributors then describe stochastic and statistical models that are used to generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field specifications and Bayesian computational inference via Gibbs sampling and Markov chain Monte Carlo, illustrating the methods with a variety of data examples, such as temperature surfaces, dioxin concentrations, ozone concentrations, and a well-established deterministic dynamical weather model. [Résumé éditeur] Statistical methods for spatio-temporal systems [livre] / Finkenstädt, B.(Ed.) ; Held, L(Ed.) ; Isham, V.(Ed.) . - Boca Raton : Chapman & Hall/CRC, 2007 . - 286 p.. - (Monographs on statistics and applied probability, 107) . ISBN : 978-1-58488-593-1 Langues : Anglais (eng) Mots-clés : Statistical methods Résumé : Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities. Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time covariance functions. The contributors then describe stochastic and statistical models that are used to generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field specifications and Bayesian computational inference via Gibbs sampling and Markov chain Monte Carlo, illustrating the methods with a variety of data examples, such as temperature surfaces, dioxin concentrations, ozone concentrations, and a well-established deterministic dynamical weather model. [Résumé éditeur]

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
68147	FIN_11_68147	Livre	Salle des ouvrages	11_Mathématiques	Sorti jusqu'au 25/05/2043

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

Public ISBD 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 Année de publication : 2006 Collection : Texts in statistical science Importance : 301 p. ISBN/ISSN/EAN : 978-1-58488-424-8 Langues : Anglais (eng) Mots-clés : Analysis of variance  Linear models  Mathematical models  R (Computer program language)  Regression analysis Résumé : 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-clés : Analysis of variance  Linear models  Mathematical models  R (Computer program language)  Regression analysis Résumé : 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	Disponibilité
68106	FAR_11_68106	Livre	Salle des ouvrages	11_Mathématiques	Sorti jusqu'au 25/05/2043

Generalized additive models: an introduction with R / Faraway, J.J. (2006)  

Public ISBD Titre : Generalized additive models: an introduction with R Type de document : livre Auteurs : Faraway, J.J. Editeur : Boca Raton : Chapman & Hall/CRC Année de publication : 2006 Collection : Texts in statistical science, 67 Importance : 392 p. ISBN/ISSN/EAN : 978-1-58488-474-3 Langues : Anglais (eng) Mots-clés : R (Computer program language)  Statistical methods Résumé : 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-clés : R (Computer program language)  Statistical methods Résumé : 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	Disponibilité
68131	FAR_11_68131	Livre	Salle des ouvrages	11_Mathématiques	Sorti jusqu'au 25/05/2043

A handbook of statistical analyses using R / Everitt, B.S. ; Hothorn, T. (2006)  

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Measurement error in nonlinear models: a modern perspective / Carroll, R.J. ; Ruppert, D. ; Stefanski, L.A. ; Crainiceanu, C.M. (2006)  

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Linear models with R / Faraway, J.J. (2005)  

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Computational statistics handbook with MATLAB / MARTINEZ, W.L. ; MARTINEZ, A.R. (2002)

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Applied nonparametric statistical methods / Sprent, P. ; Smeeton, N.C. (2001)

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Modelling and quantitative methods in fisheries / Haddon, M. (2001)

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Probability and statistical models with applications / CHARALAMBIDES, C.A.(Ed.) ; KOUTRAS, M.V.(Ed.) ; Balakrishnan, N.(Ed.) (2001)

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