32 citations · 155 across the 38 of their papers we have counts for
23 papers · 1 filter
Statistical Validation of Computer Models: Global and Subdomain Hypothesis Testing
Chaoan Li, Xianyang Zhang, Rui Tuo
Computer simulations play an important role in scientific discovery and engineering innovation. Reliable computer models enable virtual experimentation that reduces the need for co…
A General Framework for Multiple Testing via E-value Aggregation and Data-Dependent Weighting
Guanxun Li, Xianyang Zhang
Motivated by recent findings in Li and Zhang (2025), which established an equivalence between certain p-value-based multiple testing procedures and the e-Benjamini-Hochberg procedu…
Batch effect correction with sample remeasurement in highly confounded case-control studies
Hanxuan Ye, Xianyang Zhang, Chen Wang +2
Batch effects are pervasive in biomedical studies. One approach to address the batch effects is repeatedly measuring a subset of samples in each batch. These remeasured samples are…
Joint Mirror Procedure: Controlling False Discovery Rate for Identifying Simultaneous Signals
Linsui Deng, Kejun He, Xianyang Zhang
In many applications, the process of identifying a specific feature of interest often involves testing multiple hypotheses for their joint statistical significance. Examples includ…
Adaptive Testing for Alphas in High-dimensional Factor Pricing Models
Qiang Xia, Xianyang Zhang
This paper proposes a new procedure to validate the multi-factor pricing theory by testing the presence of alpha in linear factor pricing models with a large number of assets. Beca…
High Dimensional Analysis of Variance in Multivariate Linear Regression
Zhipeng Lou, Xianyang Zhang, Wei Biao Wu
In this paper, we develop a systematic theory for high dimensional analysis of variance in multivariate linear regression, where the dimension and the number of coefficients can bo…
Powerful Spatial Multiple Testing via Borrowing Neighboring Information
Linsui Deng, Kejun He, Xianyang Zhang
Clustered effects are often encountered in multiple hypothesis testing of spatial signals. In this paper, we propose a new method, termed \textit{two-dimensional spatial multiple t…
Distance and Kernel-Based Measures for Global and Local Two-Sample Conditional Distribution Testing
Jian Yan, Zhuoxi Li, Xianyang Zhang
Testing the equality of two conditional distributions is crucial in various modern applications, including transfer learning and causal inference. Despite its importance, this fund…
Likelihood-based Inference for Random Networks with Changepoints
Daniel Cirkovic, Tiandong Wang, Xianyang Zhang
Generative, temporal network models play an important role in analyzing the dependence structure and evolution patterns of complex networks. Due to the complicated nature of real n…
A General Framework for Powerful Confounder Adjustment in Omics Association Studies
Asmita Roy, Jun Chen, Xianyang Zhang
Genomic data are subject to various sources of confounding, such as demographic variables, biological heterogeneity, and batch effects. To identify genomic features associated with…
High-dimensional Change-point Detection Using Generalized Homogeneity Metrics
Shubhadeep Chakraborty, Runmin Wang, Xianyang Zhang
Change-point detection is a classical problem in statistics. We address the problem of detecting abrupt changes in the data-generating distributions of a sequence of high-dimension…
LinDA: linear models for differential abundance analysis of microbiome compositional data
Huijuan Zhou, Kejun He, Jun Chen +1
Differential abundance analysis is at the core of statistical analysis of microbiome data. The compositional nature of microbiome sequencing data makes false positive control chall…
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…
Structure Adaptive Elastic-Net
Sandipan Pramanik, Xianyang Zhang
Penalized linear regression is of fundamental importance in high-dimensional statistics and has been routinely used to regress a response on a high-dimensional set of predictors. I…
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 Metrics for Measuring Joint Dependence with Application to Causal Inference
Shubhadeep Chakraborty, Xianyang Zhang
Many statistical applications require the quantification of joint dependence among more than two random vectors. In this work, we generalize the notion of distance covariance to qu…
Empirical Bayes, SURE and Sparse Normal Mean Models
Xianyang Zhang, Anirban Bhattacharya
This paper studies the sparse normal mean models under the empirical Bayes framework. We focus on the mixture priors with an atom at zero and a density component centered at a data…
Testing mutual independence in high dimension via distance covariance
Shun Yao, Xianyang Zhang, Xiaofeng Shao
In this paper, we introduce a type test for testing mutual independence and banded dependence structure for high dimensional data. The test is constructed based on…
Testing High Dimensional Mean Under Sparsity
Xianyang Zhang
Motivated by the likelihood ratio test under the Gaussian assumption, we develop a maximum sum-of-squares test for conducting hypothesis testing on high dimensional mean vector. Th…
On the Coverage Bound Problem of Empirical Likelihood Methods For Time Series
Xianyang Zhang, Xiaofeng Shao
The upper bounds on the coverage probabilities of the confidence regions based on blockwise empirical likelihood [Kitamura (1997)] and nonstandard expansive empirical likelihood [N…