Feiyu Jiang

Feiyu Jiang (蒋斐宇)

I am Associate Professor in Department of Statistics and Data Science, School of Management at Fudan University.

My research interest lies in nonlinear time series, change-point estimation and data-driven decision-making.

Contact

Address: Rm 519, Siyuan Building (思源楼519), Fudan University.
E-mail: jiangfy[at]fudan.edu.cn

Education

2016-2021: Ph.D. in Statistics

Department of Industrial Engineering & Center for Statistical Science, Tsinghua University.

2012-2016: BEc

School of Statistics, Renmin University of China.

Publication:

Chen, H., Chen, L., Gong, H.*, Xu, K., Zhu, T., Liu, H., Jiang, F., Liao, Q. and Huang, F. Turning Cold Entities Warm: LLM-Based User and Item Interaction Simulation for Recommendations. ACM Transactions on Information Systems, to apper.
Ma, Y., Jiang, F., Zhao, Z., Yang, H. and Yu, Y. Locally Private Nonparametric Contextual Multi-armed Bandits with Transfer Learning. Journal of the American Statistical Association, in press. [pdf]
Zhao, Z., Jiang, F.* and Yu, Y. Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints. Journal of the American Statistical Association, in press. [pdf]
Jiang, F. and Tsyawo, E*. A consistent ICM-based $\chi^2$ specification test. Econometric Theory, in press. [pdf]
Zhao, Z., Jiang, F., Yu, Y. and Chen, X. High-dimensional dynamic pricing under non-stationarity: learning and earning with change-point detection. Management Science, in press. [pdf]
Kanrar, R., Jiang, F.* and Cai, Z.* (2025) Model-free Change-point Detection using AUC of a Classifier. Journal of Machine Learning Research, 26 (190), 1-50. [pdf]
Wang, F., Jiang, F., Zhao, Z., and Yu, Y. (2025) Transfer Learning for Nonparametric Contextual Dynamic Pricing. [pdf] ICML.
Yu, C., Li, D., Jiang, F. *and Zhu, K.* (2025) Matrix GARCH Model: Inference and Application. Journal of the American Statistical Association, 120(551), 1747–1762. [pdf]
Song, K., Jiang, F. *and Zhu, K. (2025) Estimation for conditional moment models based on martingale difference divergence. Journal of Time Series Analysis, 46(4), 727-747. [pdf]
Jiang, F., Zhu, C.* and Shao, X. (2024) Two-Sample and Change-Point Inference for Non-Euclidean Valued Time Series. Electronic Journal of Statistics, 18(1), 848-894. [pdf]
Jiang, F., Gao, H.* and Shao, X. (2024) Testing serial independence of object-valued time series. Biometrika, 111(3), 925-944. [pdf]
Jiang, F., Zhao, Z. and Shao, X.*(2023), Time series analysis of COVID-19 infection curve: A change-point perspective. Journal of Econometrics, 232(1), 1-17. [pdf]
Jiang, F., Wang, R.* and Shao, X. (2023), Robust Inference for Change Points in High Dimension. Journal of Multivariate Analysis , 193, 105114[pdf]
Jiang, F., Li, D., Li, W.K. and Zhu, K.*(2023), Testing and modelling for the structural change in covariance matrix time series with multiplicative form. Statistica Sinica, 33(2), 787-818. [pdf]
Jiang, F., Zhao, Z. and Shao, X.*(2022), Modelling the COVID-19 infection trajectory: A piecewise linear quantile trend model. Journal of the Royal Statistical Society Series B (Statistical Methodology) , 84(5), 1589-1607. [pdf] With discussion and rejoinder [pdf].
Zhao, Z., Jiang, F.* and Shao, X. (2022), Segmenting Time Series via Self-Normalisation. Journal of the Royal Statistical Society Series B (Statistical Methodology), 84(5), 1699-1725. [pdf] [software]
Sun, S., Zhao, Z., Jiang, F. and Shao, X. (2024) SNSeg: An R Package for Time Series Segmentation via Self-Normalization. The R Journal, 16(3), 46-66. [pdf]
Zhou, J., Jiang, F.*, Zhu, K. and Li, W.K. (2022), Time series models for realized covariance matrices based on the matrix-F distribution. Statistica Sinica, 32, 755-786. [pdf]
Jiang, F., Li, D., and Zhu, K.* (2021), Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model. Journal of Econometrics, 224(2), 306-329. [pdf] (supplement)
Ben, Y. and Jiang, F.* (2020), A note on portmanteau tests for conditional heteroscedastic models. Economics Letters, 192, 109159. [pdf]
Jiang, F., Li, D., and Zhu, K.* (2020), Non-standard inference for augmented double autoregressive models with null volatility coefficients. Journal of Econometrics, 215, 165-183. [pdf]

Working Paper:

Dai, C., Jiang, F., Li, D. and Shao, X. Diagnostic Checking for Wasserstein Autoregression. [pdf]
Tao, Y., Jiang, F., and Shao, X. Generalized Spectral Testing with Sample Splitting. [pdf]
Jiang, F. and Zhao, Z. On Non-Stationary Dynamic Pricing: Adaptivity and Optimality. [pdf]

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