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Activity Number: 269
Type: Contributed
Date/Time: Monday, August 1, 2016 : 3:05 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract #321658
Title: An Adaptive Importance Sampling Approach for Efficiently Estimating Small P-Values in Permutation Tests
Author(s): Yang Shi* and Huining Kang and Ji-Hyun Lee and Hui Jiang
Companies: University of Michigan and University of New Mexico and University of New Mexico and University of Michigan
Keywords: Permutation test ; Importance sampling ; Cross-entropy ; p-value ; conditional Bernoulli distribution ; genomics
Abstract:

Permutation tests are commonly used for estimating p-values from statistical hypothesis testing when the sampling distribution of the test statistic under the null hypothesis is not available. One critical challenge for permutation tests in genomic studies is that an enormous number of permutations is needed for obtaining reliable estimations of small p-values, which requires intensive computational efforts. In this work, we present a computationally efficient algorithm for estimating small p-values from permutation tests based on the adaptive cross-entropy method for rare event simulations. Simulation studies and application to a real dataset demonstrate that our approach achieves remarkable gains in computational efficiency comparing with existing methods.


Authors who are presenting talks have a * after their name.

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