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Statistical computing with R / Rizzo, M.L. (2008)
Titre : Statistical computing with R Type de document : livre Auteurs : Rizzo, M.L. Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2008 Collection : Computer science and data analysis series Importance : 399 p. ISBN/ISSN/EAN : 9781584885450 Langues : Anglais (eng) MotsclÃ©s : R (Computer program language) Statistical methods RÃ©sumÃ© : 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 examplesbased approach. Suitable for an introductory course in computational statistics or for selfstudy, 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. [RÃ©sumÃ© Ã©diteur] Statistical computing with R [livre] / Rizzo, M.L. .  Boca Raton : Chapman & Hall/CRC, 2008 .  399 p..  (Computer science and data analysis series) .
ISBN : 9781584885450
Langues : Anglais (eng)
MotsclÃ©s : R (Computer program language) Statistical methods RÃ©sumÃ© : 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 examplesbased approach. Suitable for an introductory course in computational statistics or for selfstudy, 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. [RÃ©sumÃ© Ã©diteur] Exemplaires (1)
Codebarres Cote Support Localisation Section DisponibilitÃ© 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)
Titre : Analysis of correlated data with SAS and R Type de document : livre Auteurs : Shoukri, M.M. ; Chaudhary, M.A. Mention d'Ã©dition : 03 Ã©d. Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2007 Importance : 295 p. ISBN/ISSN/EAN : 9781584886198 Note gÃ©nÃ©rale : Contient un CDRom Langues : Anglais (eng) MotsclÃ©s : Epidemiology Statistical methods RÃ©sumÃ© : 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 Â· The introduction of R codes for almost all of the numerous examples solved with SAS Â· A chapter devoted to the modeling and analyzing of normally distributed variables under clustered sampling designs Â· A chapter on the analysis of correlated count data that focuses on overdispersion Â· Expansion of the analysis of repeated measures and longitudinal data when the response variables are normally distributed Â· 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 Â· Exercises at the end of each chapter to enhance the understanding of the material covered Â· An accompanying CDROM 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. [RÃ©sumÃ© Ã©diteur] Analysis of correlated data with SAS and R [livre] / Shoukri, M.M. ; Chaudhary, M.A. .  03 Ã©d. .  Boca Raton : Chapman & Hall/CRC, 2007 .  295 p.
ISBN : 9781584886198
Contient un CDRom
Langues : Anglais (eng)
MotsclÃ©s : Epidemiology Statistical methods RÃ©sumÃ© : 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 Â· The introduction of R codes for almost all of the numerous examples solved with SAS Â· A chapter devoted to the modeling and analyzing of normally distributed variables under clustered sampling designs Â· A chapter on the analysis of correlated count data that focuses on overdispersion Â· Expansion of the analysis of repeated measures and longitudinal data when the response variables are normally distributed Â· 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 Â· Exercises at the end of each chapter to enhance the understanding of the material covered Â· An accompanying CDROM 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. [RÃ©sumÃ© Ã©diteur] Exemplaires (1)
Codebarres Cote Support Localisation Section DisponibilitÃ© 68107 SHO_11_68107 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043 Statistical methods for spatiotemporal systems / FinkenstÃ¤dt, B.(Ed.) ; Held, L(Ed.) ; Isham, V.(Ed.) (2007)
Titre : Statistical methods for spatiotemporal systems Type de document : livre Auteurs : FinkenstÃ¤dt, B.(Ed.) ; Held, L(Ed.) ; Isham, V.(Ed.) Editeur : Boca Raton : Chapman & Hall/CRC AnnÃ©e de publication : 2007 Collection : Monographs on statistics and applied probability, 107 Importance : 286 p. ISBN/ISSN/EAN : 9781584885931 Langues : Anglais (eng) MotsclÃ©s : Statistical methods RÃ©sumÃ© : Statistical Methods for SpatioTemporal Systems presents current statistical research issues on spatiotemporal 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 selfcontained 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. footandmouth 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 spacetime 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 wellestablished deterministic dynamical weather model. [RÃ©sumÃ© Ã©diteur] Statistical methods for spatiotemporal systems [livre] / FinkenstÃ¤dt, B.(Ed.) ; Held, L(Ed.) ; Isham, V.(Ed.) .  Boca Raton : Chapman & Hall/CRC, 2007 .  286 p..  (Monographs on statistics and applied probability, 107) .
ISBN : 9781584885931
Langues : Anglais (eng)
MotsclÃ©s : Statistical methods RÃ©sumÃ© : Statistical Methods for SpatioTemporal Systems presents current statistical research issues on spatiotemporal 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 selfcontained 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. footandmouth 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 spacetime 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 wellestablished deterministic dynamical weather model. [RÃ©sumÃ© Ã©diteur] Exemplaires (1)
Codebarres Cote Support Localisation Section DisponibilitÃ© 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)
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 : 9781584884248 Langues : Anglais (eng) MotsclÃ©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 wellstocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduatelevel 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 : 9781584884248
Langues : Anglais (eng)
MotsclÃ©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 wellstocked toolbox of methodologies, and with its unique presentation of these very modern statistical techniques, holds the potential to break new ground in the way graduatelevel courses in this area are taught. En ligne : http://books.google.fr/books?id=ODcRsWpGji4C&dq=extending+the+linear+model+with+ [...] Exemplaires (1)
Codebarres 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 : 9781584884743 Langues : Anglais (eng) MotsclÃ©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 longstanding 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 wellgrounded 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 addon package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially selfcontained, 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 GAMrelated methods and models, such as SSANOVA, Psplines, 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 : 9781584884743
Langues : Anglais (eng)
MotsclÃ©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 longstanding 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 wellgrounded 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 addon package mgcv. Each chapter includes exercises, for which complete solutions are provided in an appendix. Concise, comprehensive, and essentially selfcontained, 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 GAMrelated methods and models, such as SSANOVA, Psplines, 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)
Codebarres Cote Support Localisation Section DisponibilitÃ© 68131 FAR_11_68131 Livre Salle des ouvrages 11_Mathématiques Sorti jusqu'au 25/05/2043 PermalinkMeasurement error in nonlinear models: a modern perspective / Carroll, R.J. ; Ruppert, D. ; Stefanski, L.A. ; Crainiceanu, C.M. (2006)
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