Statistical Computing with R, Second Edition

The second edition of Statistical Computing with R is an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. It is up-to-date with the many advances made in recent years and is suitable for an introductory course in computational statistics or for self-study.

Format: Hardback
Length: 490 pages
Publication date: 21 March 2019
Publisher: Taylor & Francis Inc

The second edition of Statistical Computing with R, a comprehensive textbook on computational statistics and statistical computing, has been released. This textbook, like its predecessor, provides an excellent tutorial on the R language, illustrating programming concepts in the context of practical computational problems. It is of great interest to all specialists working on computational statistics and Monte Carlo methods for modeling and simulation.

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. These areas have evolved significantly in recent years, with new algorithms, software, and techniques being developed. The second edition of Statistical Computing with R is up-to-date with these advances, providing a comprehensive coverage of the traditional core material of these areas with an emphasis on using the R language via an examples-based approach.

The textbook provides an overview of computational statistics and an introduction to the R computing environment. It focuses on implementation rather than theory, exploring 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. New sections, exercises, and applications have been included, as well as new chapters on resampling methods and programming topics.

The textbook is accompanied by online supplements available on GitHub, including R code for all the exercises, as well as tutorials and extended examples on selected topics. It is suitable for an introductory course in computational statistics or for self-study, and provides a valuable resource for anyone interested in developing their skills in statistical computing with R.

In conclusion, the second edition of Statistical Computing with R is an excellent textbook that provides a comprehensive and up-to-date coverage of computational statistics and statistical computing. It is an invaluable resource for all specialists working in these areas, and will be of great interest to students, researchers, and practitioners alike.

Weight: 824g
Dimension: 162 x 240 x 32 (mm)
ISBN-13: 9781466553323
Edition number: 2 New edition