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Synopsis

This book bridges the latest software applications with thebenefits of modern resampling techniques

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 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 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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