论文标题
通过葡萄藤建模优化有效数量的测试
Optimizing effective numbers of tests by vine copula modeling
论文作者
论文摘要
在多个测试环境中，我们利用葡萄藤来优化有效的测试数量。众所周知，对于校准了多个测试（为了控制家庭错误率），边际测试之间的依赖项至关重要。在先前的工作中已经显示，可以利用边缘测试之间的正依赖关系，以得出松弛的Sidak型多重性校正。通过计算给定（全球）显着性水平的相应“有效数量测试”，可以方便地表达此校正。该方法也可以应用于测试统计块，以便可以通过每个块的有效测试数量来计算有效的测试数量。在目前的工作中，我们演示了如何通过取得高内置依赖性的块来优化多重测试的功率。这些块的确定将通过估计的葡萄藤模型进行。提出了一种算法，该算法使用估计的葡萄藤的信息来根据（估计）依赖关系对适当的块进行数据驱动。数值实验证明了所提出的方法的有用性。
In the multiple testing context, we utilize vine copulae for optimizing the effective number of tests. It is well known that for the calibration of multiple tests (for control of the family-wise error rate) the dependencies between the marginal tests are of utmost importance. It has been shown in previous work, that positive dependencies between the marginal tests can be exploited in order to derive a relaxed Sidak-type multiplicity correction. This correction can conveniently be expressed by calculating the corresponding "effective number of tests" for a given (global) significance level. This methodology can also be applied to blocks of test statistics so that the effective number of tests can be calculated by the sum of the effective numbers of tests for each block. In the present work, we demonstrate how the power of the multiple test can be optimized by taking blocks with high inner-block dependencies. The determination of those blocks will be performed by means of an estimated vine copula model. An algorithm is presented which uses the information of the estimated vine copula to make a data-driven choice of appropriate blocks in terms of (estimated) dependencies. Numerical experiments demonstrate the usefulness of the proposed approach.
