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

作者：Correa Machado Alexei Manso

来源：Statistics and Computing, 2015, 25(3): 669-681.

DOI：10.1007/s11222-014-9459-z

• Hypothesis testing
• Bonferroni correction
• Family-wise error rate

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.

• 出版日期2015-5