Education

Ph.D. in Statistics, Xiamen University — Sep. 2013 – Jun. 2019
B.Sc. in Statistics, Wuhan University of Technology — Sep. 2009 – Jun. 2013

Work Experience

Associate Professor, School of Statistics, Renmin University of China — Aug. 2023 – present

Lecturer, School of Statistics, Renmin University of China — Aug. 2021 – Aug. 2023

Postdoctoral Fellow, School of Statistics, Renmin University of China — Jul. 2019 – Aug. 2021

Research Interests

Network Data Analysis, Multi-Source Data Integration, Complex Data Analysis

Honors and Awards

1. First Prize, Excellent Paper Award of Statistical Research (Second Author), Chinese Statistical Association, 2021

2. Excellent Doctoral Dissertation of Fujian Province, Academic Degrees Committee of Fujian Province, 2020

Funding

NSFC Youth Science Fund (Category C): Latent Space Models Integrating Complex Multi-Source Information, Jan. 2023 – Dec. 2025, PI, 300,000 RMB

Selected Publications

1. Fan, X., Fang, K., Lan, W., & Tsai, C. L. (2025). Network varying coefficient model. Journal of the American Statistical Association, 1–12.

2. Fan, X., Liu, M., & Ma, S. (2025). Network-based modeling of emotional expressions for multiple cancers via a linguistic analysis of an online health community. The Annals of Applied Statistics, In Press.

3. Wu, Y., Lan, W., Fan, X.*, & Fang, K. (2024). Bipartite network influence analysis of a two-mode network. Journal of Econometrics, 239(2), 105562.

4. Fan, X., Lan, W., Zou, T., & Tsai, C. L. (2024). Covariance model with general linear structure and divergent parameters. Journal of Business & Economic Statistics, 42(1), 36–48.

5. Liu, M., Fan, X.*, & Ma, S.* (2024). A quantitative linguistic analysis of a cancer online health community with a smooth latent space model. The Annals of Applied Statistics, 18(1), 144–158.

6. Fan, X., Lan, W., Zou, T., & Tsai, C. L. (2024). Mutual influence regression model. Statistica Sinica, 34, 1723-1743.

7. Fan, X., Fang, K., Pu, D., & Qin, R. (2024). Generalized latent space model for one-mode networks with awareness of two-mode networks. Computational Statistics & Data Analysis, 193, 107915.

8. Sun, Y., Luo, Z., & Fan, X.* (2022). Robust structured heterogeneity analysis approach for high‐dimensional data. Statistics in Medicine, 41(17), 3229-3259.

9. Zhang, J., Fan, X.*, Li, Y.*, & Ma, S. (2022). Heterogeneous graphical model for non-negative and non-Gaussian PM2. 5 data. Journal of the Royal Statistical Society Series C: Applied Statistics, 71(5), 1303-1329.

10. Fan, X., Zhang, Q., Ma, S., & Fang, K. (2021). Conditional score matching for high-dimensional partial graphical models. Computational Statistics & Data Analysis, 153, 107066.

11. Fan, X., Fang, K., Ma, S., & Zhang, Q. (2020). Integrating approximate single factor graphical models. Statistics in Medicine, 39(2), 146-155.

12. Fan, X., Fang, K., Ma, S., Wang, S., & Zhang, Q. (2019). Assisted graphical model for gene expression data analysis. Statistics in Medicine, 38(13), 2364-2380.

13. Fan, X., Liu, M., Fang, K., Huang, Y., & Ma, S. (2017). Promoting structural effects of covariates in the cure rate model with penalization. Statistical Methods in Medical Research, 26(5), 2078-2092.

14. Fan, X., Fang, K., Zheng, C., & Zhang Z. (2021) Prediction of credit default point based on integrative cure rate model. Statistical Research, 38(2):99-113.(范新妍, 方匡南, 郑陈璐, 张志远. (2021). 基于整合治愈率模型的信贷违约时点预测. 统计研究, 38(2):99-113.)

15. Fang,K., Fan, X. & Ma, S.(2016) Forecasting of Enterprise's Credit Risk Based on Network-logistic Model. Statistical Research, 33(4):50-55.(方匡南, 范新妍, 马双鸽.(2016) 基于网络结构 Logistic 模型的企业信用风险预警. 统计研究, 33(4): 50-55.)

Software Development

HGMND, Version 2, Released 2021-04-09

https://cran.rstudio.com/bin/macosx/contrib/4.1/HGMND_0.1.0.tgz

Teaching

Undergraduate: Probability Theory, Statistics

Graduate: Multivariate Statistical Analysis