» Statistical Computing with R (Computer Science and Data Analysis)
Rating: 5.0 / 5.00 (1 review)
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Manufacturer: Chapman & Hall/CRC
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Statistical Computing with R (Computer Science and Data Analysis) Details
Binding: HardcoverDewey Decimal Number: 519.502855133
EAN: 9781584885450
ISBN: 1584885459
Label: Chapman & Hall/CRC
Manufacturer: Chapman & Hall/CRC
Number Of Items: 1
Number Of Pages: 416
Publication Date: 2007-11-15
Publisher: Chapman & Hall/CRC
Studio: Chapman & Hall/CRC
Accessories for Statistical Computing with R (Computer Science and Data Analysis)
Statistical Computing with R (Computer Science and Data Analysis) Reviews
Customer Rating:Summary: great book on statistics using R
Comment: This book is another text that covers statistical methods using R. Many of the others are specialized as for example Mark Chang's which covers adaptive designs only and Jim Albert's deals only with Bayesian methods. This is the best that I have seen to cover advanced statistical topics and teach the essentials of R for statistical analysis. So someone like me, who is not very familiar with R, can learn quickly how to apply it. The basics are covered in Chapter 1 and the author explains where to find the good R libraries and how to download the software which is free.
Chapter 2 is a short review of probability and statistics. This is provided to help the reader understand the material on generating Monte Carlo data for univariate and multivariate probability distributions that comes in Chapter 3.
The other chapters cover visualization of multivariate data, Monte Carlo methods, integration and variance reduction techniques and advanced topics including the bootstrap and the jackknife, permutation test, MCMC methods and working with data arrays and frames. All these methods are illustrated with R code.
For the most part the author provides excellent references to the literature and texts. The coverage of bootstrap is exceptional. My book, Efron and Tibshirani, Davison and Hinkley and Shao and Tu are all referenced. There is a thorough treatment of bootstrap confidence intervals covering all the high order methods and their properties. There is even a discussion about the jackknife estimate of the acceleration constant (illustrated with R code) and mention of an alternative method available in the book by Shao and Tu. The text includes 299 references.
The only glaring omission I found was in the discussion of permutation tests. There have been major books written by Good, Edgington. Manly and Pesarin that were overlooked.
Editorial Review for Statistical Computing with R (Computer Science and Data Analysis):
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.
