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

Maria L. Rizzo is a professor of statistics as well as the director and coordinator of the Actuarial Science program at Bowling Green State University. Her research interests include Statistics, Applied Statistics, Statistical Computing, Multivariate Analysis, Multivariate Inference, Goodness-of-Fit, Nonlinear Dependence, Statistical Learning, Cluster Analysis and Classification, Computational Statistics, and Energy Statistics. She is the author of two books.

Introduction. Probability and Statistics Review. Methods for Generating Random Variables. Visualization of Multivariate Data. Monte Carlo Integration and Variance Reduction. Monte Carlo Methods in Inference. Bootstrap and Jackknife. Permutation Tests. Markov Chain Monte Carlo Methods. Probability Density Estimation. Smoothing and Nonparametric Regression. High Dimensional Data. Numerical Methods in R. Optimization.