Title: Professor
Research Areas: Nonparametric and Semiparametric Modeling, High-Dimensional Data Analysis, Longitudinal Data Analysis, Clinical Trials
Courses Taught: Probability Theory, Mathematical Statistics
E-mail: huang.tao@mail.shufe.edu.cn; Phone: 65901206
Research Project
Serial Number | Project Name | Project Number | Project source | Start and End Time | Project Funding |
1 | Variable Selection in High-Dimensional Data and Its Applications in Finance and Biology | 13PJC048 | Shanghai Pujiang Talent Program | 2013/09 - 2015/08 | |
2 | Statistical Modeling and Inference of Partially Stationary Panel Data | 11871323 | National Natural Science Foundation General Program | 2019/01 – 2022/12 | |
2 | Research on Modeling, Inference, and Application of Non-Euclidean Symmetric Positive Definite Matrix Data | National Natural Science Foundation General Project | 2024/01 – 2027/12 |
Research Fields
Non-parametric and semi-parametric modeling, high-dimensional data analysis, longitudinal data analysis, clinical experiments
Educational Experience
August 1999 - May 2004 Doctor of Philosophy in Statistics, University of North Carolina
September 1995 - July 1999 Bachelor of Science in Probability and Statistics, Peking University
Work Experience
December 2012 - Present: Professor at the School of Statistics and Management, Shanghai University of Finance and Economics
August 2011 - November 2012: Quantitative Financial Analyst at Shanghai Fuan Da Information Technology Co., Ltd.
August 2006 - May 2011: Assistant Professor, Department of Statistics, University of Virginia
September 2009 - December 2009: Visiting Scholar at the Department of Operations Research and Financial Engineering, Princeton University
June 2004 - July 2006: Postdoctoral Fellow at Yale University School of Public Health
Research Achievement
1.Huang Tao, Pei Youquan, You Jinhong, Zhang Wenyang (2025). A Flexible and Parsimonious Modelling Strategy for Clustered Data Analysis, Annals of Applied Statistics, Vol 19, No. 2, 1362-1381.
2.唐一鸣，胡盛錩，龙宇航，黄涛*（2023）. 网络数据异质非参数模型的同质追踪,应用数学学报，(6): 963 -997 .
3.胡盛錩，黄涛* （2023）. 对称正定矩阵时间序列自回归模型的统计推断，数理统计与管理，已接收。
4.Zhu Nenghui, You Jinhong, Huang Tao (2022). Two-stage local rank estimation for generalised partially linear varying-coefficient models,Journal of Nonparametric Statistics, 34，707-733.
5.Long Yuhang, Huang Tao* (2022). Statistical inference on group Rasch mixture network model, Stat, 11:e436.
6.王守霞，尤进红, 黄涛*（2022）存在趋势和周期特征的非平稳时间序列的建模及其应用，中国科学数学中文版，52卷，2期，177-208.
7.Long Yuhang, Huang Tao* (2022). A note on a dynamic network model with homogeneous structure, Statistics and Probability Letters, 184, 109363.
8.唐一鸣，龙宇航，黄涛 (2021). 带隐示性变量的线性测量误差模型的统计推断, 应用数学学报，44(6), 807-827.
9.Pei Youquan, Huang Tao, Peng Heng and You Jinhong (2021). Network-based Clustering for Varying Coefficient Panel Data Models, Journal of Business & Economic Statistics , accepted.
10.Wang Shouxia, Huang Tao, You Jinhong, Cheng Ming-Yen (2021). Robust Inference for Nonstationary Time Series with Possibly Multiple Changing Periodic Structures, Journal of Business & Economic Statistics , accepted.
11.Hu Lixia, Huang Tao* and You Jinhong (2021). Robust Inference in Varying-coefficient Additive Models for Longitudinal/Functional Data, Statistica Sinica, 31 (2021), 773-796.
12.Tang Yiming, Bai Yang, Huang Tao* (2021) Network vector autoregression with individual effects, Metrika, 84(6), 875-893.
13.Pei Youquan*, Tang Yiming and Huang Tao (2020). Multivariate longitudinal model with irregular time autoregressive error process, Science China-Mathematics, 63(10), 2117–2136.
14.Hu Lixia, Huang Tao* and You Jinhong (2019). Estimation and Model Identification of Locally Stationary Varying-Coefficient Additive Models, Journal of the American Statistical Association, 114, 1191-1204.
15.Hu Lixia, Huang Tao* and You Jinhong (2019). Two-step Estimation of Time-varying Additive Model for Locally Stationary Time Series, Computational Statistics and Data Analysis, 130, 94-110.
16.Bai Fangfang Bai, Chen Xuerong, Chen Yan and Huang Tao* (2019). A general quantile residual life model for right-censored length-biased data, Scandinavian Journal of Statistics, 46，1191-1205.
17.Xu, P., Peng H. and Huang, Tao* (2018). Unsupervised learning of mixture regression models for longitudinal data, Computational Statistics and Data Analysis, 125, 44-56.
18.Zhao, L, Peng H. and Huang, Tao* (2018). Variance Estimation for Semiparametric Regression Models by Local Averaging, Test, 27(2):453-476
19.Huang, Tao. and Li, Jialiang (2018). Semiparametric Model Average Prediction in Panel Data Analysis, Journal of Nonparametric Statistics, 30(1):1-20
20.M. Cheng, T. Huang, P. Liu, and H. Peng (2018). Bias Reduction for Nonparametric and Semiparametric Regression Models, Statistica Sinica, 28, 2749-2770.
21.Pei Youquan, Huang Tao* and You Jinhong* (2018). Nonparametric model for panel data with fixed effects and locally stationary regressors, Journal of Econometrics, 202(2), 286-305.
22.Liu Zhongqiang, Ban Tao, Huang Tao* (2017). General Covariate-Adaptive Randomization Targeting Unequal Allocation Ratio, Journal of Statistical Planning and Inference, 191, 68-80.
23.Huang Tao, Peng Heng and Zhang Kun (2017). Model selection for finite Gaussian mixture models, Statistica Sinica, 27, 147-169
24.Huang Tao，Liu Zhongqiang and Hu Feifang (2013). Longitudinal covariate-adjusted response-adaptive randomization designs. Journal of Statistical Planning and Inference, 143, 1816-1827.
Book Chapters:
a)Zhu Hongjian, Huang Tao and Hyun Seung Won (2015). Adaptive randomization designs for personalized medicine, Modern Adaptive Randomized Clinical Trials: Statistical, Operational, and Regulatory Aspects, Chapter 14, CRC Press.
b)Huang Tao, Zhu Hongjian (2015). Longitudinal covariate-adjusted response-adaptive randomization: impact of delayed responses and missing data, Modern Adaptive Randomized Clinical Trials: Statistical, Operational, and Regulatory Aspects, Chapter 15, CRC Press.
