An Introduction to the Bootstrap

by Bradley Efron, R.J. Tibshirani

An Introduction to the Bootstrap arms scientists and engineers as well as statisticians with the computational techniques they need to analyze and understand complicated data sets. The bootstrap is a computer-based method of statistical inference that answers statistical questions without formulas and gives a direct appreciation of variance, bias, coverage, and other probabilistic phenomena. This book presents an overview of the bootstrap and related methods for assessing statistical accuracy, concentrating on the ideas rather than their mathematical justification. Not just for beginners, the presentation starts off slowly, but builds in both scope and depth to ideas that are quite sophisticated.

Genres: Mathematics, Textbooks, Nonfiction, Science

456 pages, Kindle Edition

First published January 1, 1993

Reviews

[Abrar · Rating 4 out of 5]

Very good statistical simulation book that helps to understand the bootstrapping approach in evaluation research methodology.

[WannaHaveShrex · Rating 4 out of 5]

Weird notation but overall a great book

[Steven Knox · Rating 5 out of 5]

This is an extraordinarily well written book. I have lost count of the number of times this book made crystal clear a point left obscure by the rest of the literature on the bootstrap, jackknife, and related nonparametric methods. I would give this six stars if I could.

Remarkably little prior knowledge is needed for the first 80% of the book (Chapters 1-20). Introductory courses in modern nonparametric statistics and asymptotic statistics, or the equivalent, are probably good to have from Chapter 21 on.

[Lin · Rating 5 out of 5]

A highly recommended read on bootstrap. The authors provide clear mathematical illustrations on the logic of bootstraps, using very well-designed examples. I found this book very helpful as an intro to the method of bootstrap.

[Leye Wang · Rating 5 out of 5]

Easy to follow and a lot of illustrative examples. Only needing some basis statistics background, you can see what bootstrap can do and what it cannot do. Most parts do not need serious math.