A0486
Title: On the testing of multiple hypothesis in sliced inverse regression
Authors: Zhigen Zhao - Temple University (United States) [presenting]
Xin Xing - Virginia Tech University (United States)
Abstract: Multiple tests of the general regression framework are considered, with the aim of studying the relationship between a univariate response and a p-dimensional predictor. To test the hypothesis of the effect of each predictor, an angular balanced statistic (ABS) is constructed based on the estimator of the sliced inverse regression without assuming a model of the conditional distribution of the response. According to the developed limiting distribution results, ABS is shown to be asymptotically symmetric with respect to zero under the null hypothesis. A model-free multiple testing procedure is proposed using angular balanced statistics (MTA), and it is theoretically shown that the false discovery rate of this method is less than or equal to a designated level asymptotically. Numerical evidence has shown that the MTA method is much more powerful than its alternatives, subject to the control of the false discovery rate.
