Mathematical Statistics with Resampling and R
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Book Overview
Resampling helps students understand the meaning of samplingdistributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how toapply modern resampling techniques to mathematical statistics.Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes thepowerful and flexible computer language R to underscore thesignificance and benefits of modern resampling techniques.
The book begins by introducing permutation tests and bootstrapmethods, motivating classical inference methods. Striking a balancebetween theory, computing, and applications, the authors exploreadditional topics such as:
Exploratory data analysisCalculation of sampling distributionsThe Central Limit TheoremMonte Carlo samplingMaximum likelihood estimation and properties of estimatorsConfidence intervals and hypothesis testsRegressionBayesian methodsThroughout the book, case studies on diverse subjects such asflight delays, birth weights of babies, and telephone companyrepair times illustrate the relevance of the real-worldapplications of the discussed material. Key definitions andtheorems of important probability distributions are collected atthe end of the book, and a related website is also available, featuring additional material including data sets, R scripts, andhelpful teaching hints.
Mathematical Statistics with Resampling and Ris an excellent book for courses on mathematical statistics at theupper-undergraduate and graduate levels. It also serves as avaluable reference for applied statisticians working in the areasof business, economics, biostatistics, and public health whoutilize resampling methods in their everyday work.
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