| A necessary and sufficient condition for edge universality at the largest singular values of covariance matrices X Ding, F Yang The Annals of Applied Probability 28 (3), 1679-1738, 2018 | 57 | 2018 |
| Singular vector and singular subspace distribution for the matrix denoising model Z Bao, X Ding, K Wang The Annals of Statistics 49 (1), 370-392, 2021 | 55 | 2021 |
| Spiked separable covariance matrices and principal components X Ding, F Yang The Annals of Statistics 49 (2), 1113-1138, 2021 | 49 | 2021 |
| High dimensional deformed rectangular matrices with applications in matrix denoising X Ding Bernoulli 26 (1), 387-417, 2020 | 44 | 2020 |
| Statistical inference for principal components of spiked covariance matrices Z Bao, X Ding, J Wang, K Wang The Annals of Statistics 50 (2), 1144-1169, 2022 | 40* | 2022 |
| Spiked sample covariance matrices with possibly multiple bulk components X Ding Random Matrices: Theory and Applications 10 (01), 2150014, 2021 | 22* | 2021 |
| Tracy-Widom distribution for heterogeneous Gram matrices with applications in signal detection X Ding, F Yang IEEE Transactions on Information Theory 68 (10), 6682-6715, 2022 | 20* | 2022 |
| Estimation and inference for precision matrices of nonstationary time series X Ding, Z Zhou The Annals of Statistics 48 (4), 2455-2477, 2020 | 17 | 2020 |
| Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud X Ding, HT Wu IEEE Transactions on Information Theory 69 (3), 1899-1931, 2023 | 14* | 2023 |
| Edge statistics of large dimensional deformed rectangular matrices X Ding, F Yang Journal of Multivariate Analysis 192, 105051, 2022 | 12 | 2022 |
| Singular vector distribution of sample covariance matrices X Ding Advances in applied probability 51 (1), 236-267, 2019 | 10 | 2019 |
| Auto-regressive approximations to non-stationary time series, with inference and applications X Ding, Z Zhou The Annals of Statistics 51 (3), 1207-1231, 2023 | 9* | 2023 |
| Local laws for multiplication of random matrices X Ding, HC Ji The Annals of Applied Probability 33 (4), 2981-3009, 2023 | 9* | 2023 |
| On the spectral property of kernel-based sensor fusion algorithms of high dimensional data X Ding, HT Wu IEEE Transactions on Information Theory 67 (1), 640-670, 2020 | 8 | 2020 |
| A Riemann--Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory X Ding, T Trogdon International Mathematics Research Notices 2024 (5), 3975–4061, 2024 | 5 | 2024 |
| Learning low-dimensional nonlinear structures from high-dimensional noisy data: An integral operator approach X Ding, R Ma The Annals of Statistics 51 (4), 1744-1769, 2023 | 4 | 2023 |
| The conjugate gradient algorithm on a general class of spiked covariance matrices X Ding, T Trogdon Quarterly of Applied Mathematics 80 (1), 99--155, 2022 | 4 | 2022 |
| How do kernel-based sensor fusion algorithms behave under high-dimensional noise? X Ding, HT Wu Information and Inference: A Journal of the IMA 13 (1), iaad051, 2024 | 3 | 2024 |
| Spiked multiplicative random matrices and principal components X Ding, HC Ji Stochastic Processes and their Applications 163, 25--60, 2023 | 3 | 2023 |
| Multivariate functional response low‐rank regression with an application to brain imaging data X Ding, D Yu, Z Zhang, D Kong Canadian Journal of Statistics 49 (1), 150-181, 2021 | 3 | 2021 |
Fan YangTsinghua UniversityVerified email at mail.tsinghua.edu.cn
