Exact Combinatorial Inference for Brain Images
Abstract
The permutation test is known as the exact test procedure in statistics. However, often it is not exact in practice and only an approximate method since only a small fraction of every possible permutation is generated. Even for a small sample size, it often requires to generate tens of thousands permutations, which can be a serious computational bottleneck. In this paper, we propose a novel combinatorial inference procedure that enumerates all possible permutations combinatorially without any resampling. The proposed method is validated against the standard permutation test in simulation studies with the ground truth. The method is further applied in twin DTI study in determining the genetic contribution of the minimum spanning tree of the structural brain connectivity.
Keywords
Cite
@article{arxiv.1807.02885,
title = {Exact Combinatorial Inference for Brain Images},
author = {Moo K. Chung and Zhan Luo and Alex D. Leow and Andrew L. Alexander and Richard J. Davidson and H. Hill Goldsmith},
journal= {arXiv preprint arXiv:1807.02885},
year = {2018}
}
Comments
Accepted for publication in MICCAI 2018