Research Interests

The primary objective of my research is to develop new statistical theories and methodologies for large-scale and high-dimensional data with complex structures. My research has focused on high dimensional/large-scale statistical inference, kernel and distance-based methods, genomics and microbiome data analysis, functional data analysis, time series, and econometrics. My research is currently supported by NIH 1R01GM144351-01, NIH 1R21HG011662, NSF DMS-2113359, and local grants from Texas A&M.

Publications and Preprints

X Zhang, T Dawn. Sequential Gradient Descent and Quasi-Newton's Method for Change-Point Analysis. AISTATS, 2023. link

S Pramanik, X Zhang. Structure Adaptive Elastic-Net. arXiv, 2023. link R code

J Yan, X Zhang. A Nonparametric Two-sample Conditional Distribution Test. arXiv, 2022. link

L Deng, K He, X Zhang. Powerful Spatial Multiple Testing via Borrowing Neighboring Information. arXiv, 2022. link

X Zhang, H Zhou, H Ye. A Modern Theory for High-dimensional Cox Regression Models. arXiv, 2022. link

D Cirkovic, T Wang, X Zhang. Likelihood-based Changepoint Detection in Preferential Attachment Networks. arXiv, 2022. link

A Roy, J Chen, X Zhang. A General Framework for Powerful Confounder Adjustment in Omics Association Studies. arXiv, 2022. link

Z Lou, X Zhang, W Wu. High Dimensional Analysis of Variance in Multivariate Linear Regression. Biometrika, to appear, 2022. link

J Yan, X Zhang. Kernel Two-Sample Tests in High Dimension: Interplay Between Moment Discrepancy and Dimension-and-Sample Orders. Biometrika, to appear, 2022. link

H Zhou, K He, J Chen, X Zhang. LinDA: Linear Models for Differential Abundance Analysis of Microbiome Compositional Data. Genome biology, 23:95, 2022. link CRAN: MicrobiomeStat

H Cao, J Chen, X Zhang. Optimal false discovery rate control for large scale multiple testing with auxiliary information. Annals of Statistics, 50 (2), 807–857, 2022. link R: OrderShapeEM

X Zhang, J Chen. Covariate adaptive false discovery rate control with applications to omics-wide multiple testing. Journal of the American Statistical Association, 117, 411-427, 2022. link R: CAMT

S Yun, X Zhang, B Li. Detection of local differences in spatial characteristics between two spatiotemporal random fields. Journal of the American Statistical Association, 117, 291-306, 2022. link

J Chen, X Zhang. D-MANOVA: fast distance-based multivariate analysis of variance for large-scale microbiome association studies. Bioinformatics 38 (1), 286-288, 2022. link CRAN: GUniFrac

S Yi, X Zhang. Projection-based Inference for High-dimensional Linear Models. Statistica Sinica, 32, 1-23, 2022. link

S Yi, X Zhang, L Yang, J Huang, Y Liu, C Wang, DJ Schaid, J Chen. 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies. Genome biology, 22:208, 2021. link supplement file R: tdfdr

H Zhou, X Zhang, J Chen. Covariate Adaptive Family-wise Error Rate Control for Genome-Wide Association Studies. Biometrika, 108, 915–931, 2021. link R: CAMT

S Chakraborty, X Zhang. High-dimensional Change-point Detection Using Generalized Homogeneity Metrics. arXiv, 2021. link

S Chakraborty, X Zhang. A new framework for distance and kernel-based metrics in high dimensions. Electronic Journal of Statistics, 15, 5455-5522, 2021. link slides

J Huang, L Bai, B Cui, L Wu, L Wang, Z An, S Ruan, Y Yu, X Zhang, J Chen. Leveraging biological and statistical covariates improves the detection power in epigenome-wide association testing. Genome biology, 21:88, 2020. link

C Zhu, X Zhang, S Yao, X Shao. Distance-based and RKHS-based dependence metrics in high dimension. The Annals of Statistics 48 (6), 3366-3394, 2020. link

CE Lee, X Zhang, X Shao. Testing conditional mean independence for functional data. Biometrika 107 (2), 331-346, 2020. link supplement file R code

S Chakraborty, X Zhang. Distance metrics for measuring joint dependence with application to causal inference. Journal of the American Statistical Association, 2019. link rdrr: jdcov

X Zhang, G Cheng. Gaussian approximation for high dimensional vector under physical dependence. Bernoulli 24 (4A), 2640-2675, 2018. link slides

S Yao, X Zhang, X Shao. Testing mutual independence in high dimension via distance covariance. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2018. link R codes supplement file

X Zhang, S Yao, X Shao. Conditional mean and quantile dependence testing in high dimension. The Annals of Statistics 46 (1), 219-246, 2018. link supplement file

X Zhang, G Cheng. Simultaneous inference for high-dimensional linear models. Journal of the American Statistical Association 112 (518), 757-768, 2017. link supplement file CRAN: SILM

X Zhang, A Bhattacharya. Empirical bayes, sure and sparse normal mean models. arXiv, 2017. link

X Zhang. Testing high dimensional mean under sparsity. arXiv, 2017. link

X Zhang. White noise testing and model diagnostic checking for functional time series. Journal of Econometrics 194 (1), 76-95, 2016. link R codes

X Zhang. Fixed-smoothing asymptotics in the generalized empirical likelihood estimation framework. Journal of Econometrics 193 (1), 123-146, 2016. link

X Zhang, X Shao. On the coverage bound problem of empirical likelihood methods for time series. Journal of the Royal Statistical Society: Series B: Statistical Methodology, 2016. link R codes

X Zhang, X Shao. Two sample inference for the second-order property of temporally dependent functional data. Bernoulli 21 (2), 909-929, 2015. link

X Zhang, X Shao. Fixed-b asymptotics for blockwise empirical likelihood. Statistica Sinica, 1179-1194, 2014. link

X Zhang, G Cheng. Bootstrapping high dimensional time series. arXiv, 2014. link

X Zhang, B Li, X Shao. Self‐normalization for Spatial Data. Scandinavian Journal of Statistics 41 (2), 311-324, 2014. link

X Zhang, X Shao. Fixed-smoothing asymptotics for time series. The Annals of Statistics 41 (3), 1329-1349, 2013. link

X Zhang, X Shao, K Hayhoe, DJ Wuebbles. Testing the structural stability of temporally dependent functional observations and application to climate projections. Electronic Journal of Statistics 5, 1765-1796, 2011. link

X Shao, X Zhang. Testing for change points in time series. Journal of the American Statistical Association 105 (491), 1228-1240, 2010. link R codes