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,
M"athematical 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 analysisCalculation of
sampling distributionsThe Central Limit TheoremMonte Carlo
samplingMaximum likelihood estimation and properties of
estimatorsConfidence intervals and hypothesis
testsRegressionBayesian 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.
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