论文标题
高维线性模型中的置换测试：实证研究
Permutation testing in high-dimensional linear models: an empirical investigation
论文作者
论文摘要
线性模型中的置换测试是一个良好的主题，其中滋扰系数的数量小于样本量。此类测试的常见方法是在滋扰协变量上消退后取消残留物。基于置换的测试特别有价值，因为它们可以极大地违反标准线性模型，例如非正常性和异方差。此外，在某些情况下，它们可以与现有的，强大的基于置换的多重测试方法结合使用。在这里，我们提出了对滋扰系数数量超过样本量的模型的置换测试。通过模拟研究了新型测试的性能。在广泛的模拟方案中，我们提出的置换方法提供了适当的I型错误率控制，这与某些竞争性测试不同，同时具有良好的功率。
Permutation testing in linear models, where the number of nuisance coefficients is smaller than the sample size, is a well-studied topic. The common approach of such tests is to permute residuals after regressing on the nuisance covariates. Permutation-based tests are valuable in particular because they can be highly robust to violations of the standard linear model, such as non-normality and heteroscedasticity. Moreover, in some cases they can be combined with existing, powerful permutation-based multiple testing methods. Here, we propose permutation tests for models where the number of nuisance coefficients exceeds the sample size. The performance of the novel tests is investigated with simulations. In a wide range of simulation scenarios our proposed permutation methods provided appropriate type I error rate control, unlike some competing tests, while having good power.
