Xinghao Qiao

Xinghao’s research is focused on (i) functional and longitudinal data analysis, (ii) high dimensional statistical inference, e.g. covariance and precision matrix estimation, variable selection, (iii) time series analysis, e.g. functional time series, high dimensional time series, (iv) statistical machine learning with applications in Business, Neuroimaging Analysis and Environmental Sciences.

Prior to joining the LSE as an Assistant Professor in Statistics, Xinghao earned his PhD in Business Statistics from Marshall School of Business at the University of Southern California, M.S. in Statistics at the University of Chicago and B.S. in Mathematics and Physics at Tsinghua University.

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.

Education

• PhD University of Southern California - Marshall School of Business (2010 — 2015)
• M.S. University of Chicago (2008 — 2010)
• B.S. Tsinghua University (2004 — 2007)

Some research papers

• Testing for White Noise in High-Dimensional Functional Time Series. Working paper, 2021.
• Hypothesis Testing in High-Dimensional Functional Graphical Models. (with Q. Fang and Q. Wang). Preprint, 2021.
• Edge Enhanced Graph Neural Networks for Link Prediction (with Y. Liu). Preprint, 2021.
• On the Modeling and Prediction of High-Dimensional Functional Time Series (with J. Chang, Q. Fang and Q. Yao). Working paper, 2021.
• Adaptive Functional Thresholding for Sparse Covariance Function Estimation in High Dimensions. (with S. Guo and Q. Fang). Preprint, 2021.
• Factor Modelling for High-Dimensional Functional Time Series. (with S. Guo and Q. Wang). Preprint, 2021.
• An Autocovariance-based Learning Framework for High-Dimensional Functional Time Series (with J. Chang, C. Chen and Q. Yao). Preprint, 2021.
• Finite Sample Theory for High-Dimensional Functional/Scalar Time Series with Applications (with Q. Fang and S. Guo). Electronic Journal of Statistics, to appear, 2021.
• CATVI: Conditional and Adaptively Truncated Variational Inference for Bayesian Nonparametrics (with Y. Liu and J. Lam). Preprint, 2020. Python code to implement the proposed method.
• On Consistency and Sparsity for High-Dimensional Functional Time Series with Application to Autoregressions (with S. Guo). Preprint, 2021.
• Functional Linear Regression: Dependence and Error Contamination (with C. Chen and S. Guo). Journal of Business and Economic Statistics, to appear, 2020.
• 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.