Zhou Chunxiao, Wang Huixia Judy, Wang Yongmei Michelle
Dept. of Electrical and Computer Eng., University of Illinois at Urbana-Champaign, Champaign, IL 61820.
Adv Neural Inf Process Syst. 2009;22:2277-2285.
In this paper, we develop an efficient moments-based permutation test approach to improve the test's computational 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.
在本文中，我们开发了一种基于矩的高效置换检验方法，通过用皮尔逊分布级数逼近检验统计量的置换分布来提高检验的计算效率。该方法涉及计算置换分布的前四个矩。我们提出了一种新颖的递归方法，从理论上和分析上推导这些矩，而无需任何置换。使用模拟数据和真实数据展示了使用不同检验统计量的实验结果。所提出的策略利用非参数置换检验和参数化皮尔逊分布逼近，以实现准确性和效率。
