Name: Han Miao
Title: Associate Professor
Research Areas: Survival Analysis, Statistical Inference for Longitudinal Data and Recurrent Events, Analysis of Complex Heterogeneous Data
Courses taught: Linear Models, Applied Survival Analysis
E - mail: han.miao@mail.shufe.edu.cn; Phone: 65901549
Research Project
Serial Number | Project Name | Project Number | Project Source | Start and End Time | Project Funding |
1 | Semi-parametric statistical analysis of longitudinal data and recurrent event data and its applications | 11601307 | National Natural Science Foundation of China Youth Project | 2017.01-2019.12 | 210,000 |
2 | Semi-Parametric Statistical Analysis of Complex Data and Its Applications in Fields such as Public Health | 2020PJC053 | Shanghai Pujiang Talent Program | 2020.11-2022.10 | 150,000 |
Research Fields
Survival analysis, statistical inference of longitudinal data and recurrent event data, analysis of complex heterogeneous data.
Education Background
2006.9-2010.6 Nankai University, School of Mathematical Sciences, Major in Information and Computing Science, Bachelor of Science
2010.9-2015.6 Institute of Mathematics and Systems Science, Chinese Academy of Sciences, Major in Probability Theory and Mathematical Statistics, Doctor of Science
Work Experience
2020.08-Present, Shanghai University of Finance and Economics, School of Statistics and Management, Associate Professor
2018.04-2020.07, Shanghai University of Finance and Economics, School of Statistics and Management, Lecturer
2017.04-2018.04, Department of Biostatistics, MD Anderson Cancer Center, University of Texas, Postdoctoral Fellow
2015.09-2017.04, Shanghai University of Finance and Economics, School of Statistics and Management, Lecturer
Han, M., Song, X., Sun, L. and Liu, L. (2014). Joint modeling of longitudinal data with informative observation times and dropouts. Statistica Sinica, 24, 1487-1504.
Han, M., Song, X., Sun, L. and Liu, L. (2016). An additive-multiplicative mean model for marker data contingent on recurrent event with an informative terminal event. Statistica Sinica, 26, 1197-1218.
Han, M., Sun, L., Liu, Y. and Zhu, J. (2018). Joint analysis of recurrent event data with additive–multiplicative hazards model for the terminal event time. Metrika, 81, 523-547.
Han, M., Han, D. and Sun, L. (2018). A class of partially linear transformation models for recurrent gap times. Communications in Statistics-Theory and Methods, 47, 739-766.
Han, D., Han, M., Huang, J., & Lin, Y. (2023). Robust inference for high‐dimensional single index models. Scandinavian Journal of Statistics, 50(4), 1590-1615.
Han, M., Lin, Y., Liu, W., & Wang, Z. (2024). Robust inference for subgroup analysis with general transformation models. Journal of Statistical Planning and Inference, 229, 106100.
Han, D., Han, M., Hao, M., Sun, L., & Wang, S. (2024). Group inference of high-dimensional single-index models. Journal of Nonparametric Statistics, online.
肖志英，刘小峰，段园家，韩邈. (2025). 右删失生存数据平均剩余寿命模型的变量选择.《应用数学学报》，online.
Duan, Y., Han, M., & Sun, L. A nested copula model for recurrent gap times with a dependent terminal event, under review.
Han, M., Cui, H., & Sun, L. A flexible joint model for longitudinal data with informative observation times and a dependent terminal event, under review.
Social Work
Statistica Sinica, Statistics in Medicine, "Science China: Mathematics" and other journal reviewers, Executive Director of the National Teaching and Research Association of Industrial Statistics Digital Economy and Blockchain Association, Director of the Resource and Environment Statistics Branch of the China Field Statistics Association.
