Mathematical Statistics With Resampling and R

by Laura M. Chihara, Tim C. Hesterberg, Tim C Hesterberg

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 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.

Genres: Mathematics

418 pages, Hardcover

First published September 6, 2011

Reviews

[Buciu Petre · Rating 5 out of 5]

Probably the best introductory statistics book you will read. It uses modern computationally-intensive resampling techniques to illustrate basic concepts of statistical inference and estimation and their application in real-world data analysis. It is very clear, with many graphs and well-written R code but the math could have been a little more rigorous in some parts.

[Rahul · Rating 4 out of 5]

Great explanations and proofs of statistical concepts. Example problems and plots from R are effective. However, the book could benefit from a larger number of problems at the end of each chapter.

The authors provide (readable) theory with some important tips regarding practical applications as well (from one of the authors' extensive applied stats work, I presume). Overall, a great intro to mathematical statistics.

P.S.: Be sure to keep track of the Errata page online (ex. One of the distribution pdfs is incorrect in the book's appendix, but the errata lists the correct pdf).

[Cael · Rating 4 out of 5]

A good introductory statistics book, with plenty of emphasis on modern techniques such as permutation tests, bootstrap distributions, etc. I didn't know any R before reading this and now I feel pretty comfortable in R. A lot of typos, mostly unimportant, but one typo in a technical book can really throw you for a while. Fortunately the authors are committed to eliminating typos in an upcoming edition, so if you can wait I'd suggest getting the next edition.