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Surrogate Data Methods Based on a Shuffling of the Trials for Synchrony Detection: The Centering Issue

机译:基于试验改组的同步检测替代数据方法:中心问题

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摘要

We investigate several distribution-free dependence detection procedures, all based on a shuffling of the trials, from a statistical point of view. The mathematical justification of such procedures lies in the bootstrap principle and its approximation properties. In particular, we show that such a shuffling has mainly to be done on centered quantities—that is, quantities with zero mean under independence—to construct correct p-values, meaning that the corresponding tests control their false positive (FP) rate. Thanks to this study, we introduce a method, named permutation UE, which consists of a multiple testing procedure based on permutation of experimental trials and delayed coincidence count. Each involved single test of this procedure achieves the prescribed level, so that the corresponding multiple testing procedure controls the false discovery rate (FDR), and this with as few assumptions as possible on the underneath distribution, except independence and identical distribution across trials. The mathematical meaning of this assumption is discussed, and it is in particular argued that it does not mean what is commonly referred in neuroscience to as cross-trials stationarity. Some simulations show, moreover, that permutation UE outperforms the trial-shuffling of Pipa and Grün (2003) and the MTGAUE method of Tuleau-Malot et al. (2014) in terms of single levels and FDR, for a comparable amount of false negatives. Application to real data is also provided.
机译:我们从统计学的角度研究了几种无分布依赖性检测程序,所有这些程序都是基于对试验的改组。这种程序的数学依据在于自举原理及其近似性质。特别是,我们表明,这种改组主要必须对中心数量(即,独立性下均值为零的数量)进行处理,以构造正确的p值,这意味着相应的测试将控制其假阳性(FP)率。多亏了这项研究,我们介绍了一种名为置换UE的方法,该方法由基于实验试验的置换和延迟的符合计数的多重测试程序组成。该过程的每个涉及的单项测试均达到规定的水平,因此相应的多重测试过程可控制错误发现率(FDR),并且对底层分布的假设要尽可能少,除了独立性和跨试验的相同分布。讨论了该假设的数学含义,特别是争论说这并不意味着在神经科学中通常称为交叉试验平稳性。此外,一些仿真表明,置换UE优于Pipa和Grün(2003)的试验改组以及Tuleau-Malot等人的MTGAUE方法。 (2014)就单一水平和FDR而言,假阴性的数量相当。还提供了对真实数据的应用。

著录项

  • 来源
    《Neural computation》 |2016年第11期|2352-2392|共41页
  • 作者单位

    Université Côte d'Azur CNRS LJAD France Melisande.Albert@unice.fr;

    Université Côte d'Azur CNRS LPMC France Yann.Bouret@unice.fr;

    Université Bretagne Loire CNRS IRMAR UMR 6625 35043 Rennes Cedex France magalie.fromont@univ-rennes2.fr;

    Université Côte d'Azur CNRS LJAD France reynaudb@unice.fr;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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