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Computational statistics and statistical computing utilize computational, graphical, and numerical methods to address statistical challenges, making the R language an excellent choice for these disciplines. This book is among the first to incorporate R in its exploration of these topics, emphasizing an examples-based approach to traditional computational statistics material. It is well-suited for introductory courses or self-study, featuring R code for all examples and notes to clarify programming concepts. The content begins with an overview of computational statistics and an introduction to the R environment, followed by a review of fundamental probability and classical statistical inference concepts. Subsequent chapters delve into specific computational statistics topics, including simulating random variables from distributions, visualizing multivariate data, Monte Carlo integration, variance reduction methods, bootstrap and jackknife techniques, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The concluding chapter showcases examples that demonstrate the application of numerical methods using R functions. By prioritizing implementation over theory, this text provides a balanced and accessible entry point into computational statistics and statistical computing.
Acquisto del libro
Statistical Computing With R, Maria Rizzo
- Lingua
- Pubblicato
- 2007
- Rilegatura
- (Copertina rigida)
Metodi di pagamento
Ancora nessuna valutazione.
- Titolo
- Statistical Computing With R
- Lingua
- Inglese
- Autori
- Maria Rizzo
- Editore
- Chapman & Hall
- Pubblicato
- 2007
- Formato
- Copertina rigida
- Pagine
- 399
- ISBN10
- 1584885459
- ISBN13
- 9781584885450
- Serie
- Tag
- Saggistica, Tecnologia & Ingegneria, Scienza e Matematica, Computer & Internet, USA, Matematica, Statistica, Elaborazione dei dati
- Descrizione
- Computational statistics and statistical computing utilize computational, graphical, and numerical methods to address statistical challenges, making the R language an excellent choice for these disciplines. This book is among the first to incorporate R in its exploration of these topics, emphasizing an examples-based approach to traditional computational statistics material. It is well-suited for introductory courses or self-study, featuring R code for all examples and notes to clarify programming concepts. The content begins with an overview of computational statistics and an introduction to the R environment, followed by a review of fundamental probability and classical statistical inference concepts. Subsequent chapters delve into specific computational statistics topics, including simulating random variables from distributions, visualizing multivariate data, Monte Carlo integration, variance reduction methods, bootstrap and jackknife techniques, permutation tests, Markov chain Monte Carlo (MCMC) methods, and density estimation. The concluding chapter showcases examples that demonstrate the application of numerical methods using R functions. By prioritizing implementation over theory, this text provides a balanced and accessible entry point into computational statistics and statistical computing.