MVPA排列方案:交叉验证领域的排列测试

J. Etzel, T. Braver
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引用次数: 40

摘要

在基于分类的fMRI分析中,排列检验被广泛用于显著性检验,但重新标记的精确方式各不相同,并且由于数据结构复杂,对于MVPA来说通常是不平凡的。在这里,我们描述了进行排列检验的两种常用方法。在第一种方案中,我们称之为“数据集智能”方案,示例在进行交叉验证之前被重新标记,而在第二种方案中,“折叠智能”方案,交叉验证的每个折叠都被独立地重新标记。虽然数据集智能方案维护了更多真实数据集的结构,但需要额外的工作来确定在实践中应该首选哪种方法,因为这两种方法通常会导致不同的零分布(以及p值)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MVPA Permutation Schemes: Permutation Testing in the Land of Cross-Validation
Permutation tests are widely used for significance testing in classification-based fMRI analyses, but the precise manner of relabeling varies, and is generally non-trivial for MVPA because of the complex data structure. Here, we describe two common means of carrying out permutation tests. In the first, which we call the "dataset-wise" scheme, the examples are relabeled prior to conducting the cross-validation, while in the second, the "fold-wise" scheme, each fold of the cross-validation is relabeled independently. While the dataset-wise scheme maintains more of the true dataset's structure, additional work is needed to determine which method should be preferred in practice, since the two methods often result in different null distributions (and so p-values).
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