AbstractPerson-centered methods are useful for studying individual differences in terms of (dis)simila'/> Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic
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Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic

机译:用效果大小测量扩展多变量距离矩阵回归和测试统计的渐近空分布

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AbstractPerson-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.]]>
机译:Abstract以人为中心的方法有助于研究多变量结果中反应谱(dis)相似性方面的个体差异。多变量距离矩阵回归(MDMR)测试反应谱(dis)关联的显著性相似性和一组使用排列测试的预测值。本文通过推导和实证验证MDMR检验统计量的渐近零分布,并提出个体结果变量的效应大小,从而扩展了MDMR,该效应大小被证明可以恢复真实的关联。这些扩展减轻了目前在MDMR中使用的置换测试的计算负担,并提供了更多信息,从而使MDMR可用于新的研究领域]>

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