32 citations · 49 across the 8 of their papers we have counts for
11 papers
Optimal False Discovery Rate Control for Large Scale Multiple Testing with Auxiliary Information
Hongyuan Cao, Jun Chen, Xianyang Zhang
Large-scale multiple testing is a fundamental problem in high dimensional statistical inference. It is increasingly common that various types of auxiliary information, reflecting t…
Kernel-Distance-Based Covariate Balancing
Xialing Wen, Ying Yan, Wenliang Pan +1
A common concern in observational studies focuses on properly evaluating the causal effect, which usually refers to the average treatment effect or the average treatment effect on…
Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies
Huijuan Zhou, Xianyang Zhang, Jun Chen
The family-wise error rate (FWER) has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase…
A New Framework for Distance and Kernel-based Metrics in High Dimensions
Shubhadeep Chakraborty, Xianyang Zhang
The paper presents new metrics to quantify and test for (i) the equality of distributions and (ii) the independence between two high-dimensional random vectors. We show that the en…
Covariate Adaptive False Discovery Rate Control with Applications to Omics-Wide Multiple Testing
Xianyang Zhang, Jun Chen
Conventional multiple testing procedures often assume hypotheses for different features are exchangeable. However, in many scientific applications, additional covariate information…
Distance-based and RKHS-based Dependence Metrics in High Dimension
Changbo Zhu, Shun Yao, Xianyang Zhang +1
In this paper, we study distance covariance, Hilbert-Schmidt covariance (aka Hilbert-Schmidt independence criterion [Gretton et al. (2008)]) and related independence tests under th…