Bootstrapping: A Nonparametric Approach to Statistical Inference

by Christopher Z. Mooney, Robert D. Duval

Bootstrapping, a computational nonparametric technique for "re-sampling," enables researchers to draw a conclusion about the characteristics of a population strictly from the existing sample rather than by making parametric assumptions about the estimator. Using real data examples from per capita personal income to median preference differences between legislative committee members and the entire legislature, Mooney and Duval discuss how to apply bootstrapping when the underlying sampling distribution of the statistics cannot be assumed normal, as well as when the sampling distribution has no analytic solution. In addition, they show the advantages and limitations of four bootstrap confidence interval methods: normal approximation, percenti

80 pages, Paperback

First published August 9, 1993

Reviews

[Henry Jackson · Rating 4 out of 5]

Good discussion.

The book is a good introduction to bootstrapping. I was able to apply what I learned from this book. It was a good read.