Efficient Moments-based Permutation Tests

Part of Advances in Neural Information Processing Systems 22 (NIPS 2009)

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Chunxiao Zhou, Huixia Wang, Yongmei Wang


In this paper, we develop an efficient moments-based permutation test approach to improve the system’s efficiency by approximating the permutation distribution of the test statistic with Pearson distribution series. This approach involves the calculation of the first four moments of the permutation distribution. We propose a novel recursive method to derive these moments theoretically and analytically without any permutation. Experimental results using different test statistics are demonstrated using simulated data and real data. The proposed strategy takes advantage of nonparametric permutation tests and parametric Pearson distribution approximation to achieve both accuracy and efficiency.