Publications

• 2024 – present
• 2020 – 2023
• 2013 – 2019
• Other publications

Journal Publications

In the list below, coauthors marked with underlines were Ph.D. students at FSU, and “2026+” marks papers accepted for publication. For additional arXiv preprints, please refer to my Google Scholar page. Selected papers grouped by topic are on the Research page.

2024 – present

• S. Park, X. Zhang, E. Slate, S. Sun and H. Yao (2026+) Dimension Reduction for Characterizing Sexual Dimorphism in Biomechanics of the Temporomandibular Joint, Biometrics. Supplementary Materials
• J. Zeng, X. Zhang and N. Hao (2026+) Second-order Sparse Sufficient Dimension Reduction with Applications to Quadratic Discriminant Analysis, Journal of the American Statistical Association (Theory and Methods). Supplementary Materials
• L. Yan, X. Zhang and Z. Zhao (2026+) Model-free multiple testing for matrix-valued predictors with false discovery control, Journal of Computational and Graphical Statistics. Supplementary Materials
• Y. Jin, X. Zhang and A. J. Molstad (2026) Kernelized discriminant analysis for joint modeling of multivariate categorical responses, Journal of Computational and Graphical Statistics, 35, 251–262.
• A. J. Molstad and X. Zhang (2026) Conditional probability tensor decompositions for multivariate categorical response regression, Journal of the American Statistical Association (Theory and Methods), 121, 1310–1323.
• J. Zeng, N. Wang and X. Zhang (2026) Envelope Inverse Regression for Dimension Reduction: A Review and New Perspectives, Journal of Systems Science and Complexity, 39, 284–308.
• X. Zhang, W. Zhao and L. X. Zhu (2026) Dimension selection in tensor decompositions and envelope models, Journal of Multivariate Analysis, 211, 105512.
• L. Yan, X. Zhang, Z. Lan, D. Bandyopadhyay and Y. Wu (2025) Variable Screening and Spatial Smoothing in Fréchet Regression with Application to Diffusion Tensor Imaging, Annals of Applied Statistics, 19, 655–679. Supplementary Materials
• C. Ying, Z. Yu and X. Zhang (2025) Distance Weighted Directional Regression for Fréchet Sufficient Dimension Reduction, Biometrics, 81, ujaf051.
• J. Li, Q. Mai and X. Zhang (2025) The Tucker Low-Rank Classification Model for Tensor Data, Statistica Sinica, 35, 1111–1132. Supplementary Materials
• Q. Mai, X. Shao, R. Wang and X. Zhang (2025) Slicing-free Inverse Regression in High-dimensional Sufficient Dimension Reduction, Statistica Sinica, 35, 1–23.
• C. E. Lee, X. Zhang and L. Li (2025) Mean Dimension Reduction and Testing for Nonparametric Tensor Response Regression, Statistica Sinica, 35, 1–19.
• S. Park, R. Zhou, X. Zhang, L. Li and L. Liu (2024) Tensor landmark analysis with application to ADNI data, Stat, 13, e70014.
• C. E. Lee and X. Zhang (2024) Conditional Mean Dimension Reduction for Tensor Time Series, Computational Statistics and Data Analysis, 199, 107998.
• K. Deng, X. Zhang and A. J. Molstad (2024) Multi-response Linear Discriminant Analysis in High Dimensions, Journal of Machine Learning Research, 25, 1–66.
• N. Wang, K. Deng, Q. Mai and X. Zhang (2024) Leveraging Independence in High-dimensional Mixed Linear Regression, Biometrics, 80, ujae103.
• N. Wang, X. Zhang and Q. Mai (2024) Statistical Analysis for a Penalized EM Algorithm in High-dimensional Mixture Linear Regression Model, Journal of Machine Learning Research, 25, 1–85.
• J. Zeng, Q. Mai and X. Zhang (2024) Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction, Journal of the American Statistical Association (Theory and Methods), 119, 343–355. Supplementary Materials R code
• 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, Transactions on Machine Learning Research.
• N. Wang, W. Wang and X. Zhang (2024) Parsimonious Tensor Discriminant Analysis, Statistica Sinica, 34, 157–180. Supplementary Materials R code
• N. Wang and X. Zhang (2024) Robust and Covariance-assisted Tensor Response Regression, Statistics and Its Interface, 17, 291–303. Supplementary Materials

2020 – 2023

• X. Zhang, K. Deng and Q. Mai (2023) Envelopes and principal component regression, Electronic Journal of Statistics, 17, 2447–2484.
• L. Li, J. Zeng and X. Zhang (2023) Generalized Liquid Association Analysis for Multimodal Data Integration, Journal of the American Statistical Association (Theory and Methods), 118, 1984–1996. Supplementary Materials R code
• I. Lee, D. Sinha, Q. Mai, X. Zhang and D. Bandyopadhyay (2023) Bayesian Regression Analysis of Skewed Tensor Responses, Biometrics, 79, 1814–1825.
• J. Zeng, X. Zhang and Q. Mai (2023) An efficient convex formulation for reduced-rank linear discriminant analysis in high dimensions, Statistica Sinica, 33, 1249–1270. Supplementary Materials
• I. W. McKeague and X. Zhang (2022) Significance testing for canonical correlation analysis in high dimensions, Biometrika, 109, 1067–1083. Supplementary Materials R code
• Q. Mai, X. Zhang, Y. Pan and K. Deng (2022) A Doubly-Enhanced EM Algorithm for Model-Based Tensor Clustering, Journal of the American Statistical Association (Theory and Methods), 117, 2120–2134. Supplementary Materials
• K. Deng and X. Zhang (2022) Tensor Envelope Mixture Model For Simultaneous Clustering and Multiway Dimension Reduction, Biometrics, 78, 1067–1079. Supplementary Materials
• J. D. Loyal, R. Zhu, Y. Cui and X. Zhang (2022) Dimension Reduction Forests: Local Variable Importance using Structured Random Forests, Journal of Computational and Graphical Statistics, 31, 1104–1113.
• K. Min, Q. Mai and X. Zhang (2022) Fast and Separable Estimation in High-dimensional Tensor Gaussian Graphical Models, Journal of Computational and Graphical Statistics, 31, 294–300.
• N. Wang, X. Zhang and B. Li (2022) Likelihood-based Dimension Folding for Tensor Data, Statistica Sinica, 32, 2405–2429.
• J. Zeng, W. Wang and X. Zhang (2021) TRES: An R Package for Tensor Regression and Envelope Algorithms, Journal of Statistical Software, 99, 1–31. R package on CRAN
• Y. Pan, Q. Mai and X. Zhang (2020) TULIP: a toolbox for linear discriminant analysis with penalties, The R Journal, 12, 61–81. R package on CRAN
• X. Zhang, C. E. Lee and X. Shao (2020) Envelopes in Multivariate Regression Models with Nonlinearity and Heteroscedasticity, Biometrika, 107, 965–981. Matlab code
• X. Zhang, Q. Mai and H. Zou (2020) The Maximum Separation Subspace in Sufficient Dimension Reduction with Categorical Response, Journal of Machine Learning Research, 21, 1–36. Matlab code
• W. Wang, X. Zhang and Q. Mai (2020) Model-based Clustering with Envelopes, Electronic Journal of Statistics, 14, 82–109.

2013 – 2019

• W. Wang, X. Zhang and L. Li (2019) Common Reducing Subspace Model and Network Alternation Analysis, Biometrics, 75, 1109–1120. Supplementary Materials R code
• Y. Pan, Q. Mai and X. Zhang (2019) Covariate-Adjusted Tensor Classification in High-dimensions, Journal of the American Statistical Association (Theory and Methods), 114, 1305–1319. Supplementary Materials
• R. Zhu, J. Zhang, R. Zhao, P. Xu, W. Zhou and X. Zhang (2019) orthoDr: semiparametric dimension reduction via orthogonality constrained optimization, The R Journal, 11, 24–37.
• Q. Mai and X. Zhang (2019) An Iterative Penalized Least Squares Approach to Sparse Canonical Correlation Analysis, Biometrics, 75, 734–744. Supplementary Materials R code
• X. Zhang and Q. Mai (2019) Efficient integration of sufficient dimension reduction and prediction in discriminant analysis, Technometrics, 61, 259–272. Supplementary Materials
• X. Zhang and Q. Mai (2018) Model-free Envelope Dimension Selection, Electronic Journal of Statistics, 12, 2193–2216.
• R. D. Cook and X. Zhang (2018) Fast Envelope Algorithms, Statistica Sinica, 28, 1179–1197.
• X. Zhang, C. Wang and Y. Wu (2018) Functional Envelope for Model-free Sufficient Dimension Reduction, Journal of Multivariate Analysis, 163, 37–50.
• X. Zhang and L. Li (2017) Tensor Envelope Partial Least Squares Regression, Technometrics, 59, 426–436. Supplementary Materials
• L. Li and X. Zhang (2017) Parsimonious Tensor Response Regression, Journal of the American Statistical Association (Theory and Methods), 112, 1131–1146. Supplementary Materials
• R. D. Cook and X. Zhang (2016) Algorithms for envelope estimation, Journal of Computational and Graphical Statistics, 25, 284–300.
• R. D. Cook and X. Zhang (2015) Foundations for Envelope Models and Methods, Journal of the American Statistical Association (Theory and Methods), 110, 599–611.
• R. D. Cook, L. Forzani and X. Zhang (2015) Envelopes and reduced-rank regression, Biometrika, 102, 439–456.
• R. D. Cook and X. Zhang (2015) Simultaneous envelopes for multivariate linear regression, Technometrics, 57, 11–25.
• R. D. Cook and X. Zhang (2014) Fused estimators of the central subspace in sufficient dimension reduction, Journal of the American Statistical Association (Theory and Methods), 109, 815–827.
• X. Zhang (2013) New developments for net-effect plots, Wiley Interdisciplinary Reviews: Computational Statistics, 5, 105–113.

Other Publications

Conference proceedings

• L. Yan, X. Zhang and Q. Mai (2025) Heterogeneous Sufficient Dimension Reduction and Subspace Clustering, Forty-second International Conference on Machine Learning (ICML 2025).

Invited book chapters

• J. Zeng and X. Zhang (2024) Tensor and multimodal data analysis, in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, Springer.
• Q. Mai and X. Zhang (2023) Statistical methods for tensor data analysis, in Springer Handbook of Engineering Statistics, 2nd Edition, Springer.

Invited discussion

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