Science Building A1514, Tencent Conference ID:992-165-109; Code:1309
Ren Haojie, Associate Professor, Shanghai Jiaotong University
Two-sample multiple testing has a wide range of applications. Most of the literature considers simultaneous tests of equality of parameters. This talk takes a different perspective and investigates the null hypotheses that the two support sets are equal. This formulation of the testing problem is motivated by the fact that in many applications where the two parameter vectors being compared are both sparse, one might be more concerned about the detection of differential sparsity structures rather than the difference in parameter magnitudes. Focusing on this type of problems, we develop a general approach, which adapts the newly proposed symmetry data aggregation tool combined with a novel double thresholding (DT) filter. The DT filter first constructs a sequence of pairs of ranking statistics that fulfill global symmetry properties, and then chooses two data-driven thresholds along the ranking to simultaneously control the false discovery rate (FDR) and maximize the number of rejections. Several applications of the methodology are given including high-dimensional linear models and Gaussian graphical models. We show that the proposed method is able to asymptotically control the FDR under certain conditions. Numerical results confirm the effectiveness and robustness of DT in FDR control and detection ability in many settings.
Brief introduction of the speaker:
Ren Haojie, Associate Professor of School of Mathematical Sciences, Shanghai Jiao Tong University, graduated from Nankai University in 2018 with a Ph.D., and then did postdoctoral research at Penn State University under the supervision of Prof. Li Runze. His research fields include statistical anomaly probing, online learning and monitoring, and high-dimensional data inference. He has published more than 10 papers in JASA, Biometrika, and other journals.