Integration of Data-Space and Statistics-Space Boundary-Based Test to Control the False Positive Rate

摘要: Many multivariate statistical methods have been applied to detect the difference between case and control population. However, it is difficult false positive rate, especially under small sample size. Traditional family-wise error rate or discovery adjusts p values based on distribution ranks of value in same multiple testing. In this paper, we investigated performance integrating Data-space boundary-based test (BBT) Statistics-space BBT a previous proposed framework called Integrative Hypothesis Tests (IHT). The classification accuracy by provides valuable information complementary from BBT. simulation results demonstrated that integration effectively controls even for small-sample-size cases. Experiments real-world dataset bipolar disorder also validated effectiveness integration.