Efficiently estimating small p-values in permutation tests using importance sampling and cross-entropy method

30 Jul 2016  ·  Shi Yang, Kang Huining, Lee Ji-Hyun, Jiang Hui ·

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 or unreliable for finite sample sizes. 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 paper, we develop a computationally efficient algorithm for evaluating small p-values from permutation tests based on an adaptive importance sampling approach, which uses the cross-entropy method for finding the optimal proposal density. Simulation studies and analysis of a real microarray dataset demonstrate that our approach achieves considerable gains in computational efficiency comparing with existing methods. read more

PDF Abstract
No code implementations yet. Submit your code now




  Add Datasets introduced or used in this paper