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*E-statistics provides powerful methods to deal with problems in
multivariate inference and analysis *Methods are implemented in R,
and readers can immediately apply them using the freely available
energy package for R *The proposed book will provide an overview of
the existing state-of-the-art in development of energy statistics
and an overview of applications. *Background and literature review
is valuable for anyone considering further research or application
in energy statistics.
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.
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