主要论文
科研论文（†学生）
（一）金融计量
1. Zhang,F., Ma,Y.†, Peng,H. (2026). A new estimator for conditional expectile-based value-at-risk of a linear predictive regression. Journal of Business & Economic Statistics.
2. Ma,Y.†, Zhang,F., Zhong,J. (2026). Robust specification testing for rank-based linear regression. Econometric Journal.
3. Zhang,F., Zhong,P. (2026). Nonparametric inference for conditional expectile functions of time series. Journal of Time Series Analysis.
4. Zhang,F., Xu,Y.†, Yuan,D., Fan,C. (2026). Testing for Granger-causality in expectiles with application to financial contagion. Journal of Nonparametric Statistics, 38, 851-875.
5. Zhang,F., Ma,Y.†, Hui,Y. (2026). A direct nonparametric estimator for EVaR of dependent financial returns. Computational Economics, 67, 991-1008.
6. Zhang,F., Tu,Y. (2025). Threshold expectile regression model with an unknown threshold for dependent data. Oxford Bulletin of Economics and Statistics, 87,815-836.
7. Zhang,F., Xie,R., Xiao,Z. (2025). Time series quantile regression kink with an unknown threshold. Econometric Reviews, 44,1275-1320.
8. Zhang,F., Xu,Y.†, Fan,C. (2023). Nonparametric inference of expectile-based value-at-risk for dependent financial returns with application to risk assessment. International Review of Financial Analysis, 90,102852.
9. 龙振环†, 张飞鹏, 周小英† (2017). 带多个变点的逐段连续线性分位数回归模型及应用. 数量经济技术经济研究,8,150-161.
（二）经济、金融与管理中应用
10. Xu,Y.†, Zhang,F., Yuan,D., Hong,Y., Liu, X. (2026). International transmission of inflation in the commodity market during crises: Evidence based on a multilayer local Gaussian correlation network. Applied Economics.
11. 张飞鹏, 徐一雄†, 陈艳 (2026). 极端事件下股票市场非线性尾部风险测度及溢出效应研究. 系统工程理论与实践, 46, 618-637.
12. Zhou, X.†, Kang, Y., Deng, Y., Zhang, F. (2026). The impacts of climate risk on carbon return: A novel asymmetric effect with expectile regression. Finance Research Letters, 94, 109627.
13. Zhou,S., Yuan,D., Zhang,F. (2025). Multiscale systemic risk spillovers in Chinese energy market: Evidence from a tail-event driven network analysis. Energy Economics, 142,108151.
14. Zhang,F., Ma,Y.†, Liu,X., Zhou,X. (2025). Revisiting the hedging and safe haven roles of gold: Evidence from quantile-on-quantile approach. North American Journal of Economics and Finance, 80,102516.
15. Zhang,F., Zhang,Y.†, Deng,Y. (2025).What drives the ‘synchrony’ and ‘asynchrony’ between China’s stock and bond markets? An adaptive Lasso-DCC-MIDAS model. International Review of Economics and Finance, 101,104206.
16. Zhang,F., Xu,Y. †, Yuan,D. (2024). Detecting financial contagion using a new nonparametric measure of comovements. International Review of Economics and Finance, 89,284-296.
17. 张飞鹏, 徐一雄†, 邹胜轩†, 陈艳 (2022). 基于LGCNET多层网络的中国A股上市公司系统性风险度量.中国管理科学, 30,13-25.
18. Chen,Y., Qiao,G., Zhang,F. (2022). Oil price volatility forecasting: Threshold effect from stock market volatility. Technological Forecasting and Social Change, 180,121704.
（三）生物信息、医学中数据分析
19. Xue,C., Zhang,F., Li, Q. (2026). Assessing reproducibility of Hi-C chromatin interactions using stratum-adjusted irreproducible discovery rate. Bioinformatics, 42, btag390.
20. Wang,X., Chang,W., Zhang,F., Fan,C. (2026). WMRNN: weighted modal regression neural networks for right censored data. Statistics in Medicine, 45: e70641.
21. Zhang,F., Chen,X.†, Liu,P., Fan,C. (2024). Weighted expectile regression neural networks for right censored data. Statistics in Medicine, 43,5100-5114.
22. Zhang,F., Li,Q.(2023).Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments. Biometrics, 79, 2272-2285.
23. Koch,H., Keller,C., Xiang,G., Giardine,B., Zhang,F., Wang,Y., Hardison,R., Li,Q. (2022). CLIMB: High-dimensional association detection in large scale genomic data. Nature Communications, 13: 6874.
24. Singh,R., Zhang,F., Li,Q. (2022). Assessing reproducibility of high-throughput experiments in the case of missing data. Statistics in Medicine, 41,1884-1899.
25. Lyu,Y., Xue,L., Zhang,F., Koch,H., Saba,L., Kechris,K., Li,Q. (2018). Condition adaptive fused graphical lasso (CFGL): an adaptive procedure for inferring condition-specific gene co-expression network. PLOS Computational Biology, 14: e1006436.
26. Li,Q, Zhang,F. (2018). A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments. Biometrics, 74,803-813.
27. Yang,T., Zhang,F., Yardimci,Y.C., Song,F., Hardison,R.C., Noble,W., Yue,F., Li,Q. (2017). HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted correlation coefficient. Genome Research, 27,1939-1949.
（四）复杂数据、大数据统计学习
28. 刘旭，任攀攀，张飞鹏，向子玉 (2026). 异质性纵向数据的亚组检验. 中国科学:数学.
29. Liu,X., Huang,J., Zhou,Y., Zhang,F., Ren,P. (2026). Subgroup testing in change-plane models and its applications to medical data. Statistica Sinica.
30. Fan,C., Li,S., Xue,M., Zhang,F. (2025). Estimating expectile-optimal treatment regimes. Statistics and Computing, 35:137.
31. Zhang,F., Huang,X.†, Fan,C. (2021). Prediction accuracy measures for time-to-event models with left-truncated and right-censored data. Journal of Statistical Computation and Simulation, 91,2764-2779.
32. Zhang,F., Yang,J.†, Ye,M. (2020). A nonparametric maximum likelihood estimation for biased-sampling data with zero-inflated truncation. Economics Letters, 194,109399.
33. Zhou,X.†, Zhang,F. (2020). Bent line quantile regression via a smoothing technique. Statistical Analysis and Data Mining, 13,216-228.
34. Fan,C., Ding,G.†, Zhang,F. (2020). A kernel nonparametric quantile estimator for right-censored competing risks data. Journal of Applied Statistics, 47,61-75.
35. Zhang,F., Peng,H., Zhou,Y. (2019). Fine-Gray proportional subdistribution hazards model for competing risks data under length-biased sampling. Statistics and Its Interface, 12, 107-122.
36. Zhang,F., Zhao,X., Zhou,Y. (2018). An embedded estimating equation for additive risk model with biased-sampling data. Science China, Mathematics, 61,1495-1518.
37. Zhang,F., Li,Q. (2017). A continuous threshold expectile model. Computational Statistics and Data Analysis, 116,49-66.
38. Zhang,F., Li,Q. (2017). Robust bent line regression. Journal of Statistical Planning and Inference, 185,41-55.
39. Zhang,F., Peng,H., Zhou,Y. (2016).Composite partial likelihood estimation for length-biased and right-censored data with competing risks. Journal of Multivariate Analysis,149,160-176.
40. Zhang,F., Tan,Z. (2015). A new nonparametric quantile estimate for length-biased data with competing risks. Economics Letters, 137,10-12.
41. Zhang,F., Chen,X., Zhou,Y. (2014). Proportional hazards models with varying coefficients for length-biased data. Lifetime Data Analysis, 20,132-157.
（五）大语言模型
42. Gao,H.†, Zhang,F., Jiang,W., Shu,J., Zheng,F., Wei,H. (2024). On the noise robustness of in-context learning for text generation. Advances in Neural Information Processing Systems (NeurIPS), 37,16569-16600.
