Book description
This book bridges the latest software applications with the benefits of modern resampling techniques
Resampling helps students understand the meaning of sampling distributions, sampling variability, Pvalues, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques to mathematical statistics. Extensively classtested 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:
Exploratory data analysis
Calculation of sampling distributions
The Central Limit Theorem
Monte Carlo sampling
Maximum likelihood estimation and properties of estimators
Confidence intervals and hypothesis tests
Regression
Bayesian methods
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 realworld 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 upperundergraduate 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.
Table of contents
 Cover
 Title
 Copyright
 Dedication
 Contents
 Preface

1 Data and Case Studies
 1.1 Case Study: Flight Delays
 1.2 Case Study: Birth Weights of Babies
 1.3 Case Study: Verizon Repair Times
 1.4 Sampling
 1.5 Parameters and Statistics
 1.6 Case Study: General Social Survey
 1.7 Sample Surveys
 1.8 Case Study: Beer and Hot Wings
 1.9 Case Study: Black Spruce Seedlings
 1.10 Studies
 1.11 Exercises
 2 Exploratory Data Analysis
 3 Hypothesis Testing
 4 Sampling Distributions

5 The Bootstrap
 5.1 Introduction to the Bootstrap
 5.2 The PlugIn Principle
 5.3 Bootstrap Percentile Intervals
 5.4 Two Sample Bootstrap
 5.5 Other Statistics
 5.6 Bias
 5.7 Monte Carlo Sampling: The “Second Bootstrap Principle”
 5.8 Accuracy of Bootstrap Distributions
 5.9 How Many Bootstrap Samples are Needed?
 5.10 Exercises
 6 Estimation
 7 Classical Inference: Confidence Intervals
 8 Classical Inference: Hypothesis Testing
 9 Regression
 10 Bayesian Methods
 11 Additional Topics
 Appendix A Review of Probability

Appendix B Probability Distributions
 B.1 The Bernoulli and Binomial Distributions
 B.2 The Multinomial Distribution
 B.3 The Geometric Distribution
 B.4 The Negative Binomial Distribution
 B.5 The Hypergeometric Distribution
 B.6 The Poisson Distribution
 B.7 The Uniform Distribution
 B.8 The Exponential Distribution
 B.9 The Gamma Distribution
 B.10 The ChiSquare Distribution
 B.11 The Student’s t Distribution
 B.12 The Beta Distribution
 B.13 The F Distribution
 B.14 Exercises
 Appendix C Distributions Quick Reference
 Solutions to OddNumbered Exercises
 Bibliography
 Index
Product information
 Title: Mathematical Statistics with Resampling and R
 Author(s):
 Release date: September 2011
 Publisher(s): Wiley
 ISBN: 9781118029855
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