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Dependence aliasing and the control of family-wise error rate in multiple hypothesis testing

机译：多重假设检验中的依赖混叠和家庭错误率的控制

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摘要

The validation of the results obtained by hypothesis testing is of special interest in applications that deal with high-dimensional sets of variables. The use of equivocated statistical methods may result in poor control of false positives. On the other hand, overconservative methods may prevent relevant findings. In this paper we define dependence aliasing as the spurious dependence relationship among variables that appears when the number of samples is lesser than the number of variables of a study. We present a novel method for estimating the adjusted p-values in applications that require multiple hypothesis testing. The method increases the statistical power of the results by exploring the dependence among the variables, while controlling false positives in strong sense. The method is compared to other relevant adjustment models such as the false discovery rate method and resampling. We illustrate the effectiveness of the method in medical imaging studies involving progressively larger sets of variables. The results show that the proposed method is able to compute adjusted p-values that are closer to the ones obtained by resampling, but at a much lower computational cost.
机译：通过假设检验获得的结果的验证在处理高维变量集的应用中特别有意义。使用模糊的统计方法可能会导致对误报的控制不佳。另一方面，过度保守的方法可能会阻止相关发现。在本文中，我们将依赖混叠定义为变量之间的虚假依赖关系，当样本数小于研究变量数时出现的虚假依赖关系。我们提出了一种新颖的方法，用于估计需要多次假设检验的应用中的调整后的p值。该方法通过探索变量之间的依存关系来增强结果的统计能力，同时从严格意义上控制误报。将该方法与其他相关的调整模型进行比较，例如错误发现率方法和重采样。我们说明了该方法在涉及逐渐变大的变量的医学影像学研究中的有效性。结果表明，该方法能够计算出更接近于重采样获得的调整后的p值，但计算成本却低得多。

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