School Seminars and Colloquia

Bootstrap P-values: Why they work so well and how to compute them.

Statistics Seminar

by Professor Chris Lloyd


Institution: Melbourne Business School, The University of Melbourne
Date: Tue 20th April 2010
Time: 1:00 PM
Location: Russell Love Theatre, Richard Berry Building, The University of Melbourne

Abstract: For standard discrete models, bootstrap P-value perform extraordinarily well, much better than asymptotics predict. I trace this to three specific mathematical properties of the bootstrap transformation. First, it corrects boundary anomalies. Second, it improves pivotality of the P-value. Third, it correctly calibrates the P-value. I then move on to how to compute bootstrap (and other more exotic P-values using importance sampling. This involves sampling whole curves from the profile distribution of the P-value. The standard recommendation for choosing the biasing distribution fails for logistic regression. I will identify why it fails and develop a better criterion which works extremely well in all examples considered.

For More Information: contact: Neil Bathia. email n.bathia@unimelb.edu.au