Selected Papers and Research Topics

Tensor Data Analysis

• A Doubly-Enhanced EM Algorithm for Model-Based Tensor Clustering (with Q. Mai, Y. Pan and K. Deng)
  Journal of the American Statistical Association (2022) 117, 2120–2134. Supplementary Materials.

• Covariate-Adjusted Tensor Classification in High-dimensions (with Y. Pan and Q. Mai)
  Journal of the American Statistical Association (2019), 114, 1305–1319. Supplementary Materials.

• Parsimonious Tensor Response Regression (with L. Li)
  Journal of the American Statistical Association (2017), 112, 1131–1146. Supplementary Materials.

Envelope Models and Methods

• Envelopes in Multivariate Regression Models with Nonlinearity and Heteroscedasticity (with C.E. Lee and X. Shao),
  Biometrika (2020), 107, 965–981.

• Envelopes and reduced-rank regression (with R. D. Cook and L. Forzani)
  Biometrika (2015), 102, 439–456.

• Foundations for Envelope Models and Methods (with R. D. Cook)
  Journal of the American Statistical Association (2015), 110, 599–611.

Dimension Reduction

• Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction (with J. Zeng and Q. Mai),
  Journal of the American Statistical Association (2024), 119, 343–355. Supplementary Materials and R code

• Generalized Liquid Association Analysis for Multimodal Data Integration (with J. Zeng and L. Li),
  Journal of the American Statistical Association (2023), 118, 1984–1996.

• Significance testing for canonical correlation analysis in high dimensions (with I. McKeague),
  Biometrika (2022), 109, 1067–1083.

• Fused Estimators of the Central Subspace in Sufficient Dimension Reduction (with R. D. Cook)
  Journal of the American Statistical Association (2014), 109, 815–827.

Journal Publications

In the list below, coauthors marked with underlines were Ph.D. students at FSU. For additional arXiv preprints, please refer to my Google Scholar page

2024 – present

• K. Deng, X. Zhang and A. J. Molstad (2024) Multi-response linear discriminant analysis in high dimensions., JMLR.
  

• L. Yan, X. Zhang, Z. Lan, D. Bandyopadyay, and Y. Wu (2024+) Variable Screening and Spatial Smoothing in Frechet Regression with Application to Diffusion Tensor Imaging, AOAS. Supplementary Materials
  

• S. Park, R. Zhou, X. Zhang, Li, L. and L. Liu (2024+) Tensor Landmark Analysis with application to ADNI data, Stat.
  

• N. Wang, X. Zhang and Q. Mai (2024) Statistical Analysis for a Penalized EM Algorithm in High-dimensional Mixture Linear Regression Model, JMLR.
  

• N. Wang, K. Deng, Q. Mai and X. Zhang (2024+) Leveraging Independence in High-dimensional Mixed Linear Regression, Biometrics.
  

• J. Zeng, Q. Mai and X. Zhang (2024) Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction, JASA. Supplementary Materials and R code
  

• Q. Mai, X. Shao, R. Wang, and X. Zhang (2024+) Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction, Statistica Sinica.

• N. Wang , W. Wang and X. Zhang (2024) Parsimonious Tensor Discriminant Analysis, Statistica Sinica. Supplementary Materials and R code
  

• N. Wang and X. Zhang (2024) Robust and Covariance-Assisted Tensor Response Regression, Statistics and Its Interface. Supplementary Materials
  

• J. Li, Q. Mai and X. Zhang (2024+) The Tucker Low-Rank Classification Model for Tensor Data, Statistica Sinica. Supplementary Materials
  

• J. Yu, Z. Kong, K. Chen, X. Zhang, Y. Chen, and L. He (2024) A Multilinear Least-Squares Formulation for Sparse Tensor Canonical Correlation Analysis, TMLR
  

• C. E. Lee, X. Zhang and L. Li (2024+) Mean Dimension Reduction and Testing for Nonparametric Tensor Response Regression, Statistica Sinica
  

• C. E. Lee and X. Zhang (2024) Conditional Mean Dimension Reduction for Tensor Time Series, CSDA
  

2020 – 2023

• X. Zhang, K. Deng , Q. Mai (2023) Envelopes and Principal Component Regression, EJS
  

• I. Lee, D. Sinha, Q. Mai, X. Zhang and D. Bandyopadhyay(2023) Bayesian regression analysis of skewed tensor responses, Biometrics.
  

• L. Li, J. Zeng and X. Zhang (2023) Generalized Liquid Association Analysis for Multimodal Data Integration, JASA. Supplementary Materials and R code
  

• J. Zeng, X. Zhang and Q. Mai (2023) An Efficient Convex Formulation for Reduced-rank Linear Discriminant Analysis in High Dimensions, Statistica Sinica. Supplementary Materials
  

• I. W. McKeague and X. Zhang (2022) Significance testing for canonical correlation analysis in high dimensions, Biometrika. Supplementary Materials and R code
  

• J. Loyal, R. Zhu, Y. Cui and X. Zhang (2022) Dimension Reduction Forests: Local Variable Importance Using Structured Random Forests, JCGS.
  

• Q. Mai, X. Zhang, Y. Pan and K. Deng (2022) A Doubly-Enhanced EM Algorithm for Model-Based Tensor Clustering, JASA.
  

• N. Wang, X. Zhang and B. Li (2022) Likelihood-based Dimension Folding for Tensor Data, Statistica Sinica.
  

• K. Min, Q. Mai and X. Zhang (2022) Fast and Separable Estimation in High-dimensional Tensor Gaussian Graphical Models, JCGS.
  

• K. Deng and X. Zhang (2022) Tensor Envelope Mixture Model For Simultaneous Clustering and Multiway Dimension Reduction, Biometrics. Supplementary Materials
  

• J. Zeng, W. Wang and X. Zhang (2021) TRES: An R Package for Tensor Regression and Envelope Algorithms, Journal of Statistical Software R package on CRAN
  

• Y. Pan, Q. Mai and X. Zhang (2020) TULIP: a toolbox for linear discriminant analysis with penalties, R Journal R package on CRAN
  

• X. Zhang, C.E. Lee and X. Shao (2020) Envelopes in Multivariate Regression Models with Nonlinearity and Heteroscedasticity, Biometrika. Matlab code
  

• X. Zhang, Q. Mai and H. Zou (2020) The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response, JMLR. Matlab code
  

• W. Wang, X. Zhang and Q. Mai (2020) Model-based Clustering with Envelopes, EJS

2014 – 2019

• Y. Pan, Q. Mai and X. Zhang (2019) Covariate-Adjusted Tensor Classification in High-dimensions, JASA Supplementary Materials.
  

• Q. Mai and X. Zhang (2019) An Iterative Penalized Least Squares Approach to Sparse Canonical Correlation Analysis, Biometrics Supplementary Materials and R code.
  

• W. Wang, X. Zhang and L. Li (2019) Common Reducing Subspace Model and Network Alternation Analysis, Biometrics Supplementary Materials and R code.
  

• X. Zhang and Q. Mai (2019) Efficient integration of sufficient dimension reduction and prediction in discriminant analysis, Technometrics Supplementary Materials.
  

• R. Zhu, J. Zhang, R. Zhao, P. Xu, W. Zhou, and X. Zhang (2019) orthoDr: Semiparametric Dimension Reduction via Orthogonality Constrained Optimization, R Journal
  

• X. Zhang and Q. Mai (2018) Model-free Envelope Dimension Selection, EJS

• R. D. Cook and X. Zhang (2018), Fast Envelope Algorithms, Statistica Sinica

• X. Zhang, C. Wang and Y. Wu (2018), Functional Envelope for Model-free Sufficient Dimension Reduction, JMVA

• L. Li and X. Zhang (2017), Parsimonious Tensor Response Regression, JASA Supplementary Materials.

• X. Zhang and L. Li (2017), Tensor Envelope Partial Least Squares Regression, Technometrics Supplementary Materials.

• R. D. Cook and X. Zhang (2016), Algorithms for Envelope Estimation JCGS

• R. D. Cook and X. Zhang (2015), Foundations for Envelope Models and Methods, JASA

• R. D. Cook, L. Forzani and X. Zhang (2015),Envelopes and reduced-rank regression, Biometrika

• R. D. Cook and X. Zhang (2015), Simultaneous envelopes for multivariate linear regression, Technometrics

• R. D. Cook and X. Zhang (2014), Fused estimators of the central subspace in sufficient dimension reduction, JASA

Other Publications

• Q. Mai and X. Zhang (2024), Statistical Methods for Tensor Data Analysis (Invited Book Chapter), Springer Handbook of Engineering Statistics 2nd Edition.

• J. Zeng and X. Zhang (2024), Tensor and Multimodal Data Analysis (Invited Book Chapter), Multimodal and Tensor Data Analytics for Industrial Systems Improvement.

• X. Zhang (2020), Invited Discussion on ‘‘Review of sparse sufficient dimension reduction’’, Statistical Theory and Related Fields, 4, 146–148.

• X. Zhang (2013), New developments for net-effect plots, WIREs: Computational Statistics, 5, 105–113.

Acknowledgement

Research is or has been supported by the National Science Foundation and the National Institutes of Health. I am currently PI of the grants NSF-DMS-2053697, NSF-DMS-2113590, and NIH-R03-DE030509.