Author(s): Maria L. Rizzo

Computers

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. This second edition continues to encompass the traditional core material of computational statistics, with an emphasis on using the R language via an examples-based approach. It includes R code for all examples and R notes to help explain the R programming concepts. This edition also features a new chapter on nonparametric regression and smoothing. 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 MathComputational 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 AuthorMaria 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. Provides an excellent tutorial on R programming techniques used in practical computational problems
Covers the most important topics in computational statistics, including Monte Carlo methods, bootstrap, MCMC, and the visualization of multivariate data
Illustrates every algorithm with at least one fully implemented example coded in R
Includes new material on optimization, nonparametric regression, smoothing, and high-dimensional data
Solutions manual and figure slides available upon qualifying course adoption.