Research

Selected Preprints

*: Student co-author under my supervision; ✉: Correspondence author.

• On robust empirical likelihood for nonparametric regression with application to regression discontinuity designs.
  Fang, Q., Guo, S., Hong, Y.* and Qiao, X.
  Major revision at Journal of Econometrics, 2025.

• MultiFun-DAG: Multivariate functional directed acyclic graph based on causal decomposition.
  Lan, T., Li, Z., Qiao, X., Li, J., Xu, H., Li, Z., Bai, L., Li, M., Tsung, F., Zhao, R. and Zhang, C.
  Major revision at INFORMS Journal on Computing, 2025.

• Convergence of covariance and spectral density estimates for high-dimensional functional time series.
  Li, B.*, Qiao, X., Wu, W. and Dette, H.
  Major revision at the Annals of Statistics, 2026.

• Large-scale multiple testing for functional network models.
  Fang, Q., Jiang, Q. and Qiao, X. (✉)
  

• Large covariance matrix estimation with factor-assisted variable clustering.
  Li, D., Qiao, X. (✉) and Yu, C.*
  

• On a new robust method of inference for general time series models.
  Wang, Z.*, Qiao, X., Li, D. and Tong, H.
  

• Time series Gaussian chain graph models.
  Fang, Q., Qiao, X. and Wang, Z.
  

• Differentially private adaptive Neyman allocation for sequential experiments.
  Zhou, Y.*, Wang, Z., Qiao, X. and Li, D.
  

• Calibrating high-dimensional effects in error structure estimation for dynamic regression via conditional moment-matching.
  Shen, C.*, Qiao, X., Guo, S. and Li, D.
  

• Inference for high-dimensional error-in-variables vector autoregression.
  Chen, C., Qiao, X. (✉) and Wang, Z.*
  

• High-dimensional volatility modeling and portfolio selection with graph representation learning.
  Shen, C.*, Liu, Y., Qiao, X. (✉) and Li, D.
  

• Testing for functional white noise in high dimensions.
  Chang, J., Jiang, Q., Qiao, X. and Yang, L.*
  

• Matrix functional factor models with partial observations.
  Wang, Z.*, Qiao, X. (✉) and Li, D.
  

• A decomposition-based framework for forecasting ERA5 three-dimensional relative humidity via Wasserstein kernel regression.
  Du, C.*, Lin, Z., Meng, C. and Qiao, X.
  

• Confidence-driven active inference with multi-LLM annotations.
  Li, D., Qiao, X., Wang, Z. and Zhou, Y.*
  

• Experimental selection correction with instrumented surrogates.
  Chen, C.*, Wang, Z., Qiao, X. and Li, D.
  

• On robust inference for null recurrent time series.
  Wang, Z.*, Qiao, X., Li, D. and Tong, H.
  

Publications

The authors’ names are sorted alphabetically in all journal publications from 2021 to the present.

• Weight-calibrated estimation for factor models of high-dimensional time series.
  Qiao, X., Wang, Z.*, Yao, Q. and Zhang, B.
  Journal of the American Statistical Association, 2026.

• Factor modelling for high-dimensional functional time series.
  Guo, S., Qiao, X. (✉), Wang, Q.* and Wang, Z.*
  Journal of Business & Economic Statistics, 2026.

• Factor-guided estimation of large covariance matrix function with conditional functional sparsity.
  Li, D., Qiao, X. (✉) and Wang, Z.*
  Journal of Econometrics, 2025.

• From sparse to dense functional data in high dimensions: Revisiting phase transitions from a non-asymptotic perspective.
  Guo, S., Li, D., Qiao, X. (✉), and Wang, Y.*
  Journal of Machine Learning Research, 2025.

• On the modeling and prediction of high-dimensional functional time series.
  Chang, J., Fang, Q.*, Qiao, X. (✉), and Yao, Q.
  Journal of the American Statistical Association, 2025.

• Deep functional factor models: Forecasting high-dimensional functional time series via Bayesian nonparametric factorization.
  Liu, Y.*, Qiao, X. (✉), Wang, L. and Pei, Y.
  The 41th International Conference on Machine Learning (ICML 2024).

• Adaptive functional thresholding for sparse covariance function estimation in high dimensions.
  Fang, Q.*, Guo, S. and Qiao, X. (✉)
  Journal of the American Statistical Association, 2024.

• An autocovariance-based learning framework for high-dimensional functional time series.
  Chang, J., Chen, C.*, Qiao, X. (✉) and Yao, Q.
  Journal of Econometrics, 2024.

• EEGNN: Edge enhanced graph neural networks with a Bayesian nonparametric graph model.
  Liu, Y.*, Qiao, X. (✉), Wang, L. and Lam, J.
  The 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023).

• On consistency and sparsity for high-dimensional functional time series with application to autoregressions.
  Guo, S. and Qiao, X. (✉)
  Bernoulli, 2023.

• CATVI: Conditional and adaptively truncated variational inference for hierarchical Bayesian nonparametric models.
  Liu, Y.*, Qiao, X. (✉) and Lam, J.
  The 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022).

• Finite sample theory for high-dimensional functional time series with applications.
  Fang, Q.*, Guo, S. and Qiao, X. (✉)
  Electronic Journal of Statistics, 2022.

• Functional linear regression: dependence and error contamination.
  Chen, C.*, Guo, S. and Qiao, X.
  Journal of Business & Economic Statistics, 2022.

• Homogeneity pursuit in single index models-based panel data analysis.
  Lian, H., Qiao, X. and Zhang, W.
  Journal of Business & Economic Statistics, 2021.

• Doubly functional graphical models in high dimensions.
  Qiao, X. (✉), Qian, C.*, James, G. and Guo, S.
  Biometrika, 2020.

• Functional graphical models.
  Qiao, X. (✉), Guo, S. and James, G.
  Journal of the American Statistical Association, 2019.

• Index models for sparsely sampled functional data.
  Radchenko, P., Qiao, X. and James, G.
  Journal of the American Statistical Association, 2015.