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Statistical Computing with R, Second Edition

  • Livre Relié
  • 490 Nombre de pages
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Praise for the First Edition:" an excellent tutorial on the R language, providing examples that illustrate programming concepts in... Lire la suite
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Description

Praise for the First Edition:" an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." Tzvetan Semerdjiev, Zentralblatt Math, 2008, Vol. 1137 "Statistical computing and computational statistics are two areas of statistics described as computational, graphical, and numerical approaches to solving statistical problems. Statistical Computing with R comprises, thorough and examples-based approach, the conventional core material of computational statistics with an emphasis on R... This book includes standard statistical computing topics using the R language... All examples in the text are realised in R. Software is actively maintained, it has good connectivity to various types of data and other systems, and it is versatile. In addition, R is very stable and reliable... The book also includes exercises and applications in all chapters, as well as coverage of recent advances including R Studio. Many examples are included, fully implemented in the R statisticalcomputing environment, and the R code for the examples can be downloaded from the author's website. Most examples and exercises apply datasets accessible in the R distribution or simulated data. The author, Maria L. Rizzo, is a Full Professor at the Department of Mathematics and Statistics of Bowling Green State University (US) and is an expert on Applied Statistics, Statistical Computing, and Energy Statistics... After finishing the book, I feel that it is a well-written text useful for biostatisticians and graduate teachers, principally because it is written by a leading expert who is engaged in statistical modelling and methodological developments and applications in the real world. In my opinion, the book is a must-have for the interested biostatistician audience."- Luca Bertolaccini, ISCB December 2019

Auteur
Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green, Ohio, where she teaches statistics, actuarial science, computational statistics, statistical programming and data science. Prior to joining the faculty at BGSU in 2006, she was Assistant Professor in the Department of Mathematics at Ohio University in Athens, Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

Résumé
Praise for the First Edition: ". . . the book serves as an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." Tzvetan Semerdjiev, Zentralblatt Math Computational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of computational statistics and an introduction to the R computing environment. Focuses on implementation rather than theory. Explores key topics in statistical computing including Monte Carlo methods in inference, bootstrap and jackknife, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. Includes new sections, exercises and applications as well as new chapters on resampling methods and programming topics. Includes coverage of recent advances including R Studio, the tidyverse, knitr and ggplot2 Accompanied by online supplements available on GitHub including R code for all the exercises as well as tutorials and extended examples on selected topics. Suitable for an introductory course in computational statistics or for self-study, Statistical Computing with R, Second Edition provides a balanced, accessible introduction to computational statistics and statistical computing. About the Author Maria Rizzo is Professor in the Department of Mathematics and Statistics at Bowling Green State University in Bowling Green, Ohio, where she teaches statistics, actuarial science, computational statistics, statistical programming and data science. Prior to joining the faculty at BGSU in 2006, she was Assistant Professor in the Department of Mathematics at Ohio University in Athens, Ohio. Her main research area is energy statistics and distance correlation. She is the software developer and maintainer of the energy package for R. She also enjoys writing books including a forthcoming joint research monograph on energy statistics.

Contenu
1. Introduction Statistical Computing The R Environment Getting Started with R and RStudioBasic SyntaxUsing the R Online Help SystemDistributions and Statistical TestsFunctions Arrays, Data Frames, and ListsFormula SpecificationsGraphicsIntroduction to ggplotWorkspace and Files Using Scripts Using PackagesUsing R Markdown and knitrExercises 2. Probability and Statistics Review Random Variables and Probability Some Discrete Distributions Some Continuous Distributions Multivariate Normal Distribution Limit Theorems StatisticsBayes' Theorem and Bayesian StatisticsMarkov Chains 3. Methods for Generating Random Variables Introduction The Inverse Transform Method The Acceptance-Rejection Method Transformation Methods Sums and Mixtures Multivariate Distributions Exercises 4. Generating Random ProcessesStochastic ProcessesBrownian MotionsExercises 5. Visualization of Multivariate Data Introduction Panel Displays Surface Plots and 3D Scatter Plots Contour Plots The Grammar of Graphics and ggplot2 Other 2D Representations of Data Principal Components AnalysisExercises 6. Monte Carlo Integration and Variance Reduction Introduction Monte Carlo IntegrationVariance Reduction Antithetic Variables Control Variates Importance Sampling Stratified Sampling Stratified Importance SamplingExercisesRCode 7. Monte Carlo Methods in Inference Introduction Monte Carlo Methods for Estimation Monte Carlo Methods for Hypothesis Tests ApplicationExercises 8. Bootstrap and JackknifeThe Bootstrap The Jackknife Bootstrap Confidence Intervals Better Bootstrap Confidence Intervals ApplicationExercises 9. Resampling ApplicationsJackknife-after-BootstrapResampling for Regression ModelsInfluenceExercises 10. Permutation Tests Introduction Tests for Equal Distributions Multivariate Tests for Equal Distributions ApplicationExercises 11. Markov Chain Monte Carlo Methods Introduction The Metropolis-Hastings Algorithm The Gibbs Sampler Monitoring Convergence ApplicationExercisesR Code 12. Probability Density Estimation Univariate Density Estimation Kernel Density Estimation Bivariate and Multivariate Density Estimation Other Methods of Density EstimationExercisesR Code 13. Introduction to Numerical Methods in RIntroductionRoot-finding in One DimensionNumerical IntegrationMaximum Likelihood ProblemsApplicationExercises 14. Optimization 401IntroductionOne-dimensional OptimizationMaximum likelihood estimation with mleTwo-dimensional Optimization The EM AlgorithmLinear Programming The Simplex Method Application Exercises 15. Programming TopicsIntroductionBenchmarking: Comparing the Execution Time of CodeProfilingObject Size, Attributes, and EqualityFinding Source CodeLinking C/C++ Code using RcppApplicationExercises

Informations sur le produit

Titre: Statistical Computing with R, Second Edition
Auteur:
Code EAN: 9781466553323
ISBN: 978-1-4665-5332-3
Format: Livre Relié
Editeur: Taylor & Francis Inc
Genre: Mathématique
nombre de pages: 490
Poids: 824g
Taille: H162mm x B240mm x T32mm
Année: 2019
Auflage: 2 New edition