Research papers (#: students under my supervision; *: students' particular projects under my supervision)

  1. X. Ding and R. Ma. Kernel spectral joint embeddings for high-dimensional noisy datasets using duo-landmark integral operators , arXiv: 2405.12317.    
  2. X. Ding, Y. Li and F. Yang. Eigenvector distributions and optimal shrinkage estimators for large covariance and precision matrices, arXiv: 2404.14751.    
  3. X. Ding and Z. Wang#. Global and local CLTs for linear spectral statistics of general sample covariance matrices when the dimension is much larger than the sample size with applications , arXiv: 2308.08646.    
  4. X. Ding , J. Xie*, L. Yu and W. Zhou. Extreme eigenvalues of sample covariance matrices under generalized elliptical models with applications, arXiv: 2303.03532.
  5. X. Ding and Z. Zhou. Simultaneous Sieve Inference for Time-Inhomogeneous Nonlinear Time Series Regression , arXiv:2112.08545.    
  6. X. Ding and J. Xie*. Tracy-Widom distribution for the edge eigenvalues of elliptical model , arXiv: 2304.07893. Revised and resubmitted to Information and Inference: A Journal of the IMA
  7. X. Ding and Z. Zhou. On the partial autocorrelation function for locally stationary time series: characterization, estimation and inference , arXiv: 2401.15778. Under revision at Biometrika    
  8. X. Ding, Y. Hu# and Z. Wang#. Two sample test for covariance matrices in ultra-high dimension , arXiv: 2312.10796. Journal of the American Statistical Association (in press)    
  9. X. Ding and H.-T. Wu. How do kernel-based sensor fusion algorithms behave under high dimensional noise? , arXiv:2111.10940. Information and Inference: A Journal of the IMA (in press)
  10. X. Ding and T. Trogdon. A Riemann--Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory , International Mathematics Research Notices , 2024(5):3975–-4061,2024.
  11. X. Ding and R. Ma. Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach , The Annals of Statistics , 51(4): 1744-1769, 2023.    
  12. X. Ding and H. Ji. Local laws for multiplication of random matrices , The Annals of Applied Probability, 33(4): 2981--3009,2023.
  13. X. Ding and H.-T. Wu. Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud, IEEE Transactions on Information Theory , 69(3):1899-1931,2023.
  14. X. Ding and Z. Zhou. Auto-regressive approximations to non-stationary time series, with inference and applications, The Annals of Statistics , 51(3):1207--1231,2023.    
  15. X. Ding and H. Ji. Spiked multiplicative random matrices and principal components, Stochastic Processes and their Applications , 163:25-60,2023.
  16. X. Ding and F. Yang. Tracy-Widom distribution for heterogeneous Gram matrices with applications in signal detection, IEEE Transactions on Information Theory , 68(10):6682-6715, 2022.    
  17. X. Ding and F. Yang. Edge statistics of large dimensional deformed rectangular matrices, Journal of Multivariate Analysis, 192:105051, 2022.
  18. Z. Bao, X. Ding, J. Wang, and K. Wang. (alphabetical order). Statistical inference for principal components of spiked covariance matrices, The Annals of Statistics, 50(2):1144-1169, 2022.
  19. X. Ding and T. Trogdon. The conjugate gradient algorithm on a general class of spiked covariance matrices, Quarterly of Applied Mathematics , 80(1):99-155, 2022.
  20. X. Ding, and F. Yang. Spiked separable covariance matrices and principal components, The Annals of Statistics, 49(2): 1113-1138, 2021.
  21. X. Ding, D. Yu, Z. Zhang, and D. Kong. Multivariate functional responses low rank regression with an application to brain imaging data, The Canadian Journal of Statistics, 49(1):150-181, 2021.
  22. Z. Bao, X. Ding, and K. Wang. (alphabetical order). Singular vector and singular subspace distribution for the matrix denoising model, The Annals of Statistics, 49(1): 370-392,2021.
  23. X. Ding. Spiked sample covariance matrices with possibly multiple bulk components, Random Matrices: Theory and Applications, 10(1):2150014, 2021.    
  24. X. Ding, and H.-T. Wu. On the spectral property of kernel-based sensor fusion algorithms of high dimensional data, IEEE Transactions on Information Theory, 67(1):640 - 670, 2021.
  25. X. Ding, and Z. Zhou. Estimation and inference for precision matrices of nonstationary time series, The Annals of Statistics, 48(4):2455-2477, 2020.
  26. X. Ding. High dimensional deformed rectangular matrices with applications in matrix denoising, Bernoulli, 26(1): 387-417, 2020.    
  27. X. Ding. Singular vector distribution of sample covariance matrices, Advances in Applied Probability,51(1): 236-267, 2019.
  28. X. Ding, and F. Yang. A necessary and sufficient condition for edge universality at the largest singular values of covariance matrices, The Annals of Applied Probability, 28(3):1679-1738, 2018.