I got my PhD from Brown University in 2007 under the supervision of Professor Donald McClure and Professor Charles (Chip) Lawrence. My thesis is on image processing and statistical genetics (using hidden Markov models). I spent 7 years at Nanyang Technological University in Singapore, then 2 years at University of New South Wales in Sydney, and start working at the City University of Hong Kong in 2016 as an associate professor. My current research interest include semiparametric regression models, distributed estimation for large data, quantile regression, high-dimensional data analysis, Bayesian computation.
Recent Publications
Tianhai Zu, Heng Lian, Brittany Green and Yan Yu, Ultra-high dimensional quantile regression for longitudinal data: an application to blood pressure analysis, Journal of the American Statistical Association, accepted
Shaobo Li, Yan Yu, Shaonan Tian, Xiaorui Zhu and Heng Lian, Corporate default probability: A discrete single-index hazard model approach, Journal of Business and Economic Statistics, accepted
Xiaoyu Zhang, Di Wang, Heng Lian and Guodong Li, Nonparametric quantile regression for homogeneity pursuit in panel data models, Journal of Business and Economic Statistics, accepted
Jiamin Liu and Heng Lian, On optimal learning with random features, IEEE Transactions on Neural Networks and Learning Systems, accepted
Heng Lian, Distributed learning of conditional quantiles in the reproducing kernel Hilbert space, Neural Information Processing Systems (NeurIPS), 2022
Yingying Zhang, Yan-Yong Zhao and Heng Lian, Statistical rates of convergence for functional partially linear support vector machines for classification, Journal of Machine Learning Research, 23:1-24,2022
Di Wang, Yao Zheng, Heng Lian and Guodong Li, High-dimensional vector autoregressive time series modeling via tensor decomposition, Journal of the American Statistical Association, 117:1338-1356, 2022
Shaogao Lv and Heng Lian, Debiased distributed learning for sparse partial linear models in high dimensions, Journal of Machine Learning Research, 23:1-32,2022
Ke Yuan, Heng Lian and Wenyang Zhang, High dimensional dynamic covariance matrices with homogeneous structure, Journal of Business and Economic Statistics, 40:96-110, 2022
Heng Lian, Jiamin Liu and Zengyan Fan, : Distributed learning for sketched kernel regression, Neural Networks, 143:368-376,2021
Fode Zhang, Rui Li and Heng Lian, : Approximate nonparametric quantile regression in reproducing kernel Hilbert spaces via random projection, Information Sciences, 547:244-254, 2021
Brittany Green, Heng Lian, Yan Yu and Tianhai Zu, Ultra-high dimensional semiparametric longitudinal data analysis, Biometrics, 77:903-913,2021
Heng Lian, Xinghao Qiao and Wenyang Zhang, Homogeneity pursuit in single index models based panel data analysis, Journal of Business and Economic Statistics, 39:386-401, 2021
Submitted Manuscripts
Collaborative inference for sparse quantile regression over a decentralized network
Decentralized statistical inference for high-dimensional smoothed quantile regression
Kernel-based adaptive Huber regression with heavy-tails and contamination