Xinghao Qiao Associate Professor of Statistics

Office: Columbia House, Room 5.15 Mail: Department of Statistics London School of Economics Houghton Street London, WC2A 2AE United Kingdom Phone: +44 (0)20 7955 6880 Email: x.qiao@lse.ac.uk

Education

• PhD in Business Statistics, Marshall School of Bussiness, University of Southern California, Los Angeles, CA, USA.

• M.S. in Statistics, University of Chicago, Chicago, IL, USA.

• B.S. in Mathematics and Physics, Academic Talent Program, Tsinghua University, Beijing, China.

Teaching

• ST311 Artificial Intelligence
  

• ST443 Machine Learning and Data Mining
  

• ST498 Capstone Project
  

• ST300 Regression and Generalised Linear Models
  

Research interests

• Functional data analysis: high-dimensional functional data, partially observed functional data, functional synchronization.
  

• High-dimensional statistics: statistical inference under sparsity, graphical models, concentration inequalities.

• Complex time series analysis: high-dimensional functional time series, high-dimensional factor models, spectral analysis.
  

• Bayesian nonparametrics: conditional variational inference, topic modelling, nonparametric network models.
  

• Statistical machine learning in business applications.
  

Some research papers

• Inference for High-Dimensional Error-in-Variables Vector Autoregression (with C. Chen). Working paper, 2022.

• Large-Scale Multiple Testing of Cross-Covariance Functions with Applications to Functional Network Models (with Q. Jiang). Working paper, 2022.

• Inference for High-Dimensional Linear Regression with Correlated Errors (with S. Guo and Z. Ho). Working paper, 2022.

• Functional Graphical Model Estimation with False Discovery Rate Control. Working paper, 2022.

• EEGNN: Edge Enhanced Graph Neural Networks (with Y. Liu, L. Wang, and J. Lam). Preprint, 2022.

• On the Modeling and Prediction of High-Dimensional Functional Time Series (with J. Chang, Q. Fang and Q. Yao). Preprint, 2022.

• Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions. (with S. Guo and Q. Fang). Journal of the American Statistical Association, 2022, in revision.

• Factor Modelling for High-Dimensional Functional Time Series. (with S. Guo and Q. Wang). Preprint, 2022.

• An Autocovariance-based Learning Framework for High-Dimensional Functional Time Series (with J. Chang, C. Chen and Q. Yao). Journal of Econometrics, 2021, in revision.

• Finite Sample Theory for High-Dimensional Functional/Scalar Time Series with Applications (with Q. Fang and S. Guo). Electronic Journal of Statistics, 2022, 16, 527-591.

• CATVI: Conditional and Adaptively Truncated Variational Inference for Bayesian Nonparametrics (with Y. Liu and J. Lam). Artificial Intelligence and Statistics, 2022, to appear. Python code to implement the proposed method.

• On Consistency and Sparsity for High-Dimensional Functional Time Series with Application to Autoregressions (with S. Guo). Bernoulli, 2022, to appear. Supplimentary material is available here.

• Functional Linear Regression: Dependence and Error Contamination (with C. Chen and S. Guo). Journal of Business and Economic Statistics, 2022, 40, 444-457.

• Doubly Functional Graphical Models in High Dimensions (with C. Qian, G. James and S. Guo). Biometrika, 2020, 107: 415-431.

• Homogeneity Pursuit in Single Index Models based Panel Data Analysis (with H. Lian and W. Zhang). Journal of Business and Economic Statistics, 2021, 39, 386-401.

• Functional Graphical Models (with S. Guo and G. James). Journal of the American Statistical Association, 2019, 114, 211-222.

• Index Models for Sparsely Sampled Functional Data (with P. Radchenko and G. James). Journal of the American Statistical Association, 2015, 110, 824-836.

• Invited discussion of "Clustering Random Curves Under Spatial Dependence" (with G. James and W. Sun). Technometrics, 2012, 54, 123-126.

Recent and upcoming presentations