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

  • Jin-Xiong Lv
  • Shikui Tu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

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

Keywords

Integrative Hypothesis Test Boundary-based test False positive rate Multivariate statistical method Joint-SNVs analysis Bipolar disorder 

Notes

Acknowledgement

This work was supported by a grant from Shanghai Jiao Tong University, NO. WF220403029.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina

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