教育背景

2013.09-2019.06，厦门大学统计学专业，理学博士学位

2009.09-2013.06，武汉理工大学统计学专业，理学学士学位

 

工作经历

2023.08 至今，太阳成集团tyc122cc，副教授

2021.08-2023.08，太阳成集团tyc122cc，讲师

2019.07-2021.08，太阳成集团tyc122cc，博士后

 

研究方向

网络数据分析、多源数据分析、复杂数据分析

 

荣誉奖励

1. 首届《统计研究》优秀论文一等奖，第二获奖人，中国统计学会，2021

2. 福建省优秀博士学位论文，福建省学位委员会，2020

 

科研项目

1. 国家自然科学基金委员会, 青年科学基金项目(C类)[原青年科学基金项目], 融合复杂多源信息的潜空间模型, 2023-01-01 至 2025-12-31, 30万元, 在研, 主持

 

科研发表

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. 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. 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.

 

软件包开发

1. HGMND，2，20210409，https://cran.rstudio.com/bin/macosx/contrib/4.1/HGMND_0.1.0.tgz

 

教学课程

1. 本科课程：概率论、统计学

2. 研究生课程：多元统计分析