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Statistical computing with R
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Statistical computing with R/ Maria L. Rizzo, Bowling Green State University, Bowling Green, Ohio, U.S.A. |
| 개인저자 | Rizzo, Maria L.,author. |
| 형태사항 | 1 online resource (xvi, 399 pages): illustrations. |
| 총서사항 | Chapman & Hall/CRC computer science and data analysis series |
| 기타형태 저록 | Print version: Rizzo, Maria L. Statistical computing with R. Boca Raton : Chapman & Hall/CRC, ?008 9781584885450 |
| ISBN | 9781322627052 1322627053 9781420010718 1420010719 |
| 서지주기 | Includes bibliographical references (pages 375-393) and index. |
| 내용주기 | Probability and statistics review -- Methods for generating random variables -- Visualization of multivariate data -- Monte Carlo integration and variance reduction -- Monte Carlo methods in inference -- Bootstrap and jackknife -- Permutation tests -- Markov chain Monte Carlo methods -- Probability density estimation -- Numerical methods in R -- A. Notation -- B. Working with data frames and arrays. |
| 요약 | 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. |
| 일반주제명 | R (Computer program language) Mathematical statistics --Computer programs. Mathematical statistics --Data processing. MATHEMATICS --Applied. MATHEMATICS --Probability & Statistics --General. Mathematical statistics --Computer programs. Mathematical statistics --Data processing. R (Computer program language) Programmeertalen. Statistiek. Dataprocessing. R (computerprogramma) R Statistische Analyse |
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