Efficient Moments-Based Permutation Tests: A Framework, Methods and Applications

98 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2009.The key and only assumption for permutation tests is data exchangeability. In real applications, the data exchangeability condition is not always valid. In order to preserve the exchangeability condition required in permutation tests, we propose a new blockwise permutation test approach based on the moments of the test statistic. The accuracy and efficiency of the proposed method are demonstrated through simulated experiments and magnetic resonance imaging (MRI) brain data, including the multi-site voxel-based morphometry analysis from structural MRI (sMRI) and activation detection from functional MRI (fMRI).U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD

Efficient Blockwise Permutation Tests Preserving Exchangeability

In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to neuroimaging studies. In order to preserve the exchangeability condition required in permutation tests, we divide the entire set of data into certain exchangeability blocks. In addition, computationally efficient moments-based permutation tests are performed by approximating the permutation distribution of the test statistic with the Pearson distribution series. This involves the calculation of the first four moments of the permutation distribution within each block and then over the entire set of data. The accuracy and efficiency of the proposed method are demonstrated through simulated experiment on the magnetic resonance imaging (MRI) brain data, specifically the multi-site voxel-based morphometry analysis from structural MRI (sMRI)

Efficient Blockwise Permutation Tests Preserving Exchangeability

In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to neuroimaging studies. In order to preserve the exchangeability condition required in permutation tests, we divide the entire set of data into certain exchangeability blocks. In addition, computationally efficient moments-based permutation tests are performed by approximating the permutation distribution of the test statistic with the Pearson distribution series. This involves the calculation of the first four moments of the permutation distribution within each block and then over the entire set of data. The accuracy and efficiency of the proposed method are demonstrated through simulated experiment on the magnetic resonance imaging (MRI) brain data, specifically the multi-site voxel-based morphometry analysis from structural MRI (sMRI)