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 Math
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. 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 Author
Maria 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.