Mathematical statistics with resampling and R

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively class-tested to ensure an accessible presentation, Mathematical Statistics with Resampling and R utilizes the powerful and flexible computer language R to underscore the significance and benefits of modern resampling techniques.

The book begins by introducing permutation tests and bootstrap methods, motivating classical inference methods. Striking a balance between theory, computing, and applications, the authors explore additional topics such as.

Calculation of sampling distributions.

The Central Limit Theorem.

Maximum likelihood estimation and properties of estimators.

Confidence intervals and hypothesis tests.

Throughout the book, case studies on diverse subjects such as flight delays, birth weights of babies, and telephone company repair times illustrate the relevance of the real-world applications of the discussed material. Key definitions and theorems of important probability distributions are collected at the end of the book, and a related website is also available, featuring additional material including data sets, R scripts, and helpful teaching hints.

Mathematical Statistics with Resampling and R is an excellent book for courses on mathematical statistics at the upper-undergraduate and graduate levels. It also serves as a valuable reference for applied statisticians working in the areas of business, economics, biostatistics, and public health who utilize resampling methods in their everyday work.

1 Data and Case Studies -- 2 Exploratory Data Analysis -- 3 Hypothesis Testing -- 4 Sampling Distributions -- 5 The Bootstrap -- 6 Estimation -- 7 Classical Inference: Confidence Intervals -- 8 Classical Inference: Hypothesis Testing -- 9 Regression -- 10 Bayesian Methods -- 11 Additional Topics.