Functional graphical models X Qiao, S Guo, GM James Journal of the American Statistical Association 114 (525), 211-222, 2019 | 108 | 2019 |
Doubly functional graphical models in high dimensions X Qiao, C Qian, GM James, S Guo Biometrika 107 (2), 415-431, 2020 | 45 | 2020 |
Homogeneity pursuit in single index models based panel data analysis H Lian, X Qiao, W Zhang Journal of Business & Economic Statistics 39 (2), 386-401, 2021 | 22 | 2021 |
Index models for sparsely sampled functional data P Radchenko, X Qiao, GM James Journal of the American Statistical Association 110 (510), 824-836, 2015 | 21 | 2015 |
An autocovariance-based learning framework for high-dimensional functional time series J Chang, C Chen, X Qiao, Q Yao Journal of Econometrics 239 (2), 105385, 2024 | 18 | 2024 |
Functional linear regression: dependence and error contamination C Chen, S Guo, X Qiao Journal of Business & Economic Statistics 40 (1), 444-457, 2022 | 17 | 2022 |
On consistency and sparsity for high-dimensional functional time series with application to autoregressions S Guo, X Qiao Bernoulli 29 (1), 451-472, 2023 | 16 | 2023 |
Factor modelling for high-dimensional functional time series S Guo, X Qiao, Q Wang arXiv preprint arXiv:2112.13651, 2021 | 13 | 2021 |
Finite sample theory for high-dimensional functional/scalar time series with applications Q Fang, S Guo, X Qiao Electronic Journal of Statistics 16 (1), 527-591, 2022 | 11 | 2022 |
Adaptive functional thresholding for sparse covariance function estimation in high dimensions Q Fang, S Guo, X Qiao Journal of the American Statistical Association, 1-13, 2023 | 4 | 2023 |
Factor-guided estimation of large covariance matrix function with conditional functional sparsity D Li, X Qiao, Z Wang arXiv preprint arXiv:2311.02450, 2023 | 2 | 2023 |
From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective S Guo, D Li, X Qiao, Y Wang arXiv preprint arXiv:2306.00476, 2023 | 2 | 2023 |
CATVI: Conditional and adaptively truncated variational inference for hierarchical bayesian nonparametric models Y Liu, X Qiao, J Lam International Conference on Artificial Intelligence and Statistics, 3647-3662, 2022 | 2 | 2022 |
EEGNN: edge enhanced graph neural network with a Bayesian nonparametric graph model Y Liu, X Qiao, L Wang, J Lam International Conference on Artificial Intelligence and Statistics, 2132-2146, 2023 | 1 | 2023 |
Supplement to “On consistency and sparsity for high-dimensional functional time series with application to autoregressions.” S Guo, X Qiao | 1 | 2023 |
Conditional variational inference with adaptive truncation for Bayesian nonparametric models JY Liu, X Qiao arXiv preprint arXiv:2001.04508, 2020 | 1 | 2020 |
A General Theory for Large-Scale Curve Time Series via Functional Stability Measure S Guo, X Qiao arXiv preprint arXiv:1812.07619, 2018 | 1 | 2018 |
DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series Y Liu, X Qiao, Y Pei, L Wang arXiv preprint arXiv:2305.14543, 2023 | | 2023 |
EEGNN: Edge Enhanced Graph Neural Networks. Y Liu, X Qiao, L Wang, J Lam CoRR, 2022 | | 2022 |
Sparseness in functional data analysis X Qiao University of Southern California, 2015 | | 2015 |