Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.
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.
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.
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.