A0375
Title: Ensemble methods for testing a global null
Authors: Yaowu Liu - Southwestern University of Finance and Economics (China) [presenting]
Abstract: Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact that no uniformly most powerful test exists, prior and/or domain knowledge is commonly used to focus on a certain class of alternatives to improve the testing power. However, it is generally challenging to develop tests that are particularly powerful against a certain class of alternatives. Motivated by the success of ensemble learning methods for prediction or classification, an ensemble framework is proposed for testing that mimics the spirit of random forests to deal with the challenges. The ensemble testing framework aggregates a collection of weak base tests to form a final ensemble test that maintains strong and robust power. The framework for four problems is applied to global testing in different classes of alternatives arising from whole genome sequencing (WGS) association studies. Specific ensemble tests are proposed for each of these problems, and their theoretical optimality is established in terms of Bahadur efficiency. Extensive simulations and analysis of a real WGS dataset are conducted to demonstrate the type I error control and/or power gain of the proposed ensemble tests.
