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, 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:
- 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 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.
LAURA CHIHARA
, PhD, is Professor of Mathematics at Carleton College. She has
extensive experience teaching mathematical statistics and applied
regression analysis. She has supervised undergraduates working on
statistics projects for local businesses and organizations such as
Target Corporation and the Minnesota Pollution Control Agency. Dr.
Chihara has experience with S+ and R from her work at Insightful
Corporation (formerly 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 bootstrap
and related resampling techniques, Monte Carlo simulation methodology,
modern regression, tectonic deformation estimation, and electric
demand forecasting.
