Research

*Student author.

Publications

1. Min, K.*, Mai, Q. and Li, J.* (2023). Optimality for high-dimensional tensor discriminant analysis. Pattern Recognition, accepted.

2. Wang, N., Ren, S.* and Mai, Q. (2023). Linear discriminant analysis with sparse and dense signals. Statistica Sinica, accepted.

3. Li, J.* and Mai, Q. (2023). Model-based low-rank tensor clustering. Journal of the Graphical and Computational Statistics, accepted.

4. Mai, Q., Shao, X., Wang, R., and Zhang, X. (2023). Slicing-free High-Dimensional Sufficient Dimension Reduction with the Martingale Difference Divergence Matrix. Statistica Sinica, accepted.

5. Wang, B., Zhou, L., Yang, J. and Mai, Q. (2023). Density-Convoluted Tensor Support Vector Machines. Statistics and Its Interface, accepted.

6. Hou, X.*, Mai, Q. and Zou, H. (2023). Tensor mixture discriminant analysis with applications to sensor array data analysis. Annals of Applied Statistics, accepted.

7. Li, J.*, Mai, Q. and Zhang, X. (2023). The Tucker low-rank classification on tensor data. Statistica Sinica, accepted.

8. Zeng, J.*, Mai, Q. and Zhang, X. (2022). Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction. Journal of the American Statistical Association, accepted.

9. Mai, Q., He, D. and Zou, H. (2022). Coordinatewise Gaussianization: theories and applications. Journal of the American Statistical Association, accepted.

10. Mai, Q., Zhang, X., Pan, Y.*, and Deng, K. * (2022). A Doubly-Enhanced EM Algorithm for Model-Based Tensor Clustering. Journal of the American Statistical Association, 117, 2120-2134.

11. Lee, I.*, Mai, Q., Sinha, D., Zhang, X. and Bandyopadhyay, D. (2022). Bayesian Regression Analysis of Skewed Tensor Responses. Biometrics, accepted.

12. Ren, S.* and Mai, Q. (2022). The robust nearest shrunken centroid classifier for high-dimensional heavy-tailed data. Electronic Journal of Statistics, 16, 3343-3384.

13. Zeng, J.*, Zhang, X. and Mai, Q. (2023). An efficient convex formulation for reduced-rank linear discriminant analysis in high dimensions. Statistica Sinica, 33, 1249–1270.

14. Min, K.* and Mai, Q. (2022). A general framework for tensor screening through smoothing. Electronic Journal of Statistics, 16, 451-497.

15. Min, K.*, Mai, Q. and Zhang, X. (2022). Fast and Separable Estimation in High-dimensional Tensor Gaussian Graphical Models. Journal of Computational and Graphical Statistics, 31, 294-300.

16. Pan, Y. *, Mai, Q. and Zhang, X. (2021). TULIP: A toolbox for linear discriminant analysis with penalties. The R journal, 12, 61-18.

17. Mai, Q. and Zhang, X. (2021). Statistical Methods for Tensor Data Analysis. In Hoang Pham (Ed.), Springer Handbook of Engineering Statistics (second edition). London: Springer London, 2023. 817–829.

18. Zhang, X., Mai, Q. and Zou, H. (2020). The maximum separation subspace in sufficient dimension reduction with categorical response. Journal of Machine Learning Research, 19, 1-36.

19. Pan, Y.*, Mai, Q. and Zhang, X. (2019). Covariate-adjusted tensor classification in high dimensions. Journal of the American Statistical Association, 114, 1305-1319. [Link][Software]

20. Pan, Y.* and Mai, Q. (2019). Efficient computation for differential networks with applications to quadratic discriminant analysis. Computational Statistics and Data Analysis, 144.

21. Wang, W.*, Zhang, X. and Mai, Q. (2019). Parsimonious model-based clustering with envelopes. Electronic Journal of Statistics, 14, 82-109.

22.  Mai, Q. and Zhang, X. (2019). An iterative penalized least squares approach to sparse canonical correlation analysis. Biometrics, 75, 734–744. [Link][Software]

23. Zhang, X. and Mai, Q. (2019). Efficient integration of sufficient dimension reduction and prediction in discriminant analysis. Technometrics, 61, 259–272. [Link]

24. Mai, Q. , Yang, Y. and Zou, H. (2019). Multiclass sparse discriminant analysis. Statistica Sinica, 29, 97-111. [Link][Software]

25. Zhang, X. and Mai, Q. (2018). Model-free envelope dimension selection. Electronic Journal of Statistics, 12, 2193-2216. [Link]

26. Mai, Q. and Zou, H. (2015). The fused Kolmogorov filter: a nonparametric model-free screening method. The Annals of Statistics, 43, 1471-1497. [Link]

27. Mai, Q. and Zou, H. (2015). Semiparametric sparse discriminant analysis. Journal of Multivariate Analysis, 35, 175-188. [Link]

28. Mai, Q., and Zou, H. (2014). Nonparametric variable transformation in sufficient dimension reduction. Technometrics, 57, 1-10. [Link]

29. Mai, Q. (2013). A review of discriminant analysis in high dimensions. Wiley Interdisciplinary Reviews: Computational Statistics, 5, 190-197. [Link]

30. Mai, Q., and Zou, H. (2013). A note on the equivalence of three sparse linear discriminant methods. Technometrics, 55, 243-246. [Link]

31. Mai, Q., and Zou, H. (2013). The Kolmogorov filter for variable screening in high-dimensional binary classification. Biometrika, 100, 229-234. [Link]

32. Mai, Q., Zou, H. and Yuan, M. (2012). A direct approach to sparse discriminant analysis in ultra-high dimensions. Biometrika, 99, 29-42. [Link] [Software] [A more comprehensive technical report.]

Acknowledgement

My research is partially supported by CCF-1908969 and CCF-1617691, National Science Foundation.