Importance Sampling for Estimating Exact Probabilities in Permutational Inference

Publisher Website
Google Scholar
Abstract
This article discusses importance sampling as an alternative to conventional Monte Carlo sampling for estimating exact significance levels in a broad class of two-sample tests, including all of the linear rank tests (with or without censoring), homogeneity tests based on the chi-squared, hypergeometric, and likelihood ratio statistics, the Mantel—Haenszel trend test, and Zelen's test for a common odds ratio in several 2 × 2 contingency tables. Inference is based on randomly selecting 2 × k contingency tables from a reference set of all such tables with fixed marginals. Through a network algorithm, the tables are selected in proportion to their importance for reducing the variance of the estimated Monte Carlo p-value. Spectacular gains, up to four orders of magnitude, are achieved relative to conventional Monte Carlo sampling. The technique is illustrated on four real data sets.

This publication has 0 references indexed in Scilit: