The Romano–Wolf multiple-hypothesis correction in Stata

The Stata Journal Promoting communications on statistics and Stata · 2020 · 249 citations

• Computer Science
• Data Mining
• Statistics

When considering multiple-hypothesis tests simultaneously, standard statistical techniques will lead to overrejection of null hypotheses unless the multiplicity of the testing framework is explicitly considered. In this article, we discuss the Romano–Wolf multiple-hypothesis correction and document its implementation in Stata. The Romano–Wolf correction (asymptotically) controls the familywise error rate, that is, the probability of rejecting at least one true null hypothesis among a family of hypotheses under test. This correction is considerably more powerful than earlier multiple-testing procedures, such as the Bonferroni and Holm corrections, given that it takes into account the dependence structure of the test statistics by resampling from the original data. We describe a command, rwolf, that implements this correction and provide several examples based on a wide range of models. We document and discuss the performance gains from using rwolf over other multiple-testing procedures that control the familywise error rate.

Exact tests via multiple data splitting

Statistics & Probability Letters · 2020 · 33 citations

• Mathematics
• Statistics
• Algorithm