Bootstrapping A Nonparametric Approach to Statistical Inference

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, percentiMooney, Christopher Z. is the author of 'Bootstrapping A Nonparametric Approach to Statistical Inference' with ISBN 9780803953819 and ISBN 080395381X.