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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:
Contact:
Address: Rm 519, Siyuan Building (思源楼519), Fudan University.
E-mail: jiangfy[at]fudan.edu.cn
Education
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.
Kanrar, R., Jiang, F.* and Cai, Z.* Model-free Change-point Detection using AUC of a Classifier. [pdf] Journal of Machine Learning Research. to appear.
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.* Matrix GARCH Model: Inference and Application. Journal of the American Statistical Association, in press. [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:
Ma, Y., Jiang, F., Zhao, Z., Yang, H. and Yu, Y. Locally Private Nonparametric Contextual Multi-armed Bandits with Transfer Learning. [pdf]
Zhao, Z., Jiang, F. and Yu, Y. Contextual Dynamic Pricing: Algorithms, Optimality, and Local Differential Privacy Constraints. [pdf]
Zhao, Z., Jiang, F., Yu, Y. and Chen, X. High-dimensional dynamic pricing under non-stationarity: learning and earning with change-point detection. [pdf]
Jiang, F. and Tsyawo, E. A consistent ICM-based $\chi^2$ specification test. [pdf]
Teaching:
Teaching:
2021-2022,2022-2023,2023-2024: Probability and Mathematical Statistics， Computational Statistics
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