Mathematical Statistics with Resampling and R

This groundbreaking book shows how apply modern resampling techniques to mathematical statistics. The book includes permutation tests and bootstrap methods and classical inference methods. Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The use of R throughout the book underscores the significance of resampling since its implementation is fast enough to be both convenient and explanatory. While computer clock speeds have leveled off, new multi-core computers are well suited for parallel applications like resampling. The book contains examples, figures, exercise sets, case studies, and helpful remarks. An author-maintained web site is available.

Laura Chihara, PhD, is professor of Mathematics at Carleton College. She has extensive experience teaching mathemetical statistics and applied regression analysis. She ha supervised undergraduates working on statistics projects for local business and organizations such as Targer Corporation and the Minnesota Pollution Control Agency. Dr. Chihara has experiance with S and R from her work at Insightful Corporation (formeely MathSoft) and in statistical consulting. Tim Hesterberg, PhD, is Senior Ads Quality Statistician at Google. He was a senior research scientist for Insightful Corporation and led the development of S_ Resample and other S+ and R software. Dr. Hesterberg has published numerous articles in the areas of boorstrap and related resampling techniques, Monte Carlo simulation methodology modern regression, tectonic deformation estimation, and electri demand forecasting.