Rank-order versus mean based statistics for neuroimaging
Rank-order versus mean based statistics for neuroimaging
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DOI：
10.1016/j.neuroimage.2006.12.043
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发表时间：
2007-05-01
期刊：
影响因子：
5.7
通讯作者：
Nichols, Thomas E.
中科院分区：
文献类型：
--
作者：
Rorden, Chris;Bonilha, Leonardo;Nichols, Thomas E.
神经影像学数据的传统分析使用参数统计，如t检验。这些检验旨在检测平均差异。事实上，即使是非参数技术，如统计非参数映射（SnPM），也使用基于均值的t统计量来衡量效应大小。我们注意到，当均值不是集中趋势的准确度量时，这些度量对于检测差异可能不是特别敏感-例如，如果其中一个组正在经历天花板或地板效应（导致偏斜的数据分布）。在这里，我们介绍了一种非参数的神经影像学数据分析方法，是基于数据的排序（因此，比t检验的离群值的影响较小）。我们认为，这种方法可能会提供一个小的好处，数据集的t检验的假设已经被违反，例如数据集，其中一组的数据表现出一个偏斜的分布，由于地板或天花板效应。(c)2007爱思唯尔公司All rights reserved.
Traditional analysis of neuroimaging data uses parametric statistics, such as the t-test. These tests are designed to detect mean differences. In fact, even nonparametric techniques such as Statistical non-parametric Mapping (SnPM) use the mean-based t statistic to measure effect size. We note that these measures may not be particularly sensitive for detecting differences when the mean is not an accurate measure of central tendency - for example if one of the groups is experiencing a ceiling or floor effect (causing a skewed data distribution). Here we introduce a nonparametric approach for neuroimaging data analysis that is based on the rank-order of data (and is therefore less influenced by outliers than the t-test). We suggest that this approach may offer a small benefit for datasets where the assumptions of the t-test have been violated, for example datasets where data from one of the groups exhibits a skewed distribution due to floor or ceiling effects. (c) 2007 Elsevier Inc. All rights reserved.