Journal Papers
| 1. | Cheng Yu, Dong Li, Feiyu Jiang, and Ke Zhu. 2025. Matrix GARCH model: Inference and application. Journal of the American Statistical Association 120(551) 1747-1762.  |
| 2. | Kunyang Song, Feiyu Jiang, and Ke Zhu. 2025. Estimation for conditional moment models based on martingale difference divergence. Journal of Time Series Analysis 46(4) 727-747. |
| 3. | Rohit Kanrar, Feiyu Jiang, Zhanrui Cai. 2025. Model-free change-point detection using AUC of a classifier. Journal of Machine Learning Research 26(190) 1-50. |
| 4. | Feiyu Jiang, Hanjia Gao, and Xiaofeng Shao. 2024. Testing serial independence of object-valued time series. Biometrika 111(3) 925–944. |
| 5. | Feiyu Jiang, Changbo Zhu, and Xiaofeng Shao. 2024. Two-sample and change-point inference for non-Euclidean valued time series. Electronic Journal of Statistics 18(1) 848-894. |
| 6. | Feiyu Jiang, Dong Li, Wai Keung Li, and Ke Zhu. 2023. Testing and modelling for the structural change in covariance matrix time series with multiplicative form. Statistica Sinica 33(2) 787-818. |
| 7. | Feiyu Jiang, Runmin Wang, and Xiaofeng Shao. 2023. Robust inference for change points in high dimension. Journal of Multivariate Analysis 193 1-23. |
| 8. | Feiyu Jiang, Zifeng Zhao, and Xiaofeng Shao. 2023. Time series analysis of COVID-19 infection curve: A change-point perspective. Journal of Econometrics 232(1) 1-17. |
| 9. | Feiyu Jiang, Zifeng Zhao, and Xiaofeng Shao. 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. |
| 10. | Zifeng Zhao, Feiyu Jiang, and Xiaofeng Shao. 2022. Segmenting time series via self-normalisation. Journal of the Royal Statistical Society Series B-Statistical Methodology 84(5) 1699–1725. |
| 11. | Jiayuan Zhou, Feiyu Jiang, Ke Zhu, and Wai Keung Li. 2022. Time series models for realized covariance matrices based on the matrix-f distribution. Statistica Sinica 32(2) 755-786. |
| 12. | Feiyu Jiang, Dong Li, and Ke Zhu. 2021. Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model. Journal of Econometrics 224(2) 306-329. |
| 13. | Youhong Ben and Feiyu Jiang. 2020. A note on Portmanteau tests for conditional heteroscedastistic models. Economics Letters 192 1-5. |
| 14. | Feiyu Jiang ,Dong Li, and Ke Zhu. 2020. Non-standard inference for augmented double autoregressive models with null volatility coefficients. Journal of Econometrics 215(1) 165-183. |
Research Projects
2025.01—2029.12, Member, Research on Innovation and Application of AI Marketing Technology: The Perspective of Creation Capability of AI, National Natural Science Foundation of China
2024.01—2028.12, Member, Statistical Inference of Multi-source Heterogeneous Data based on Information Fusion and Migration, National Natural Science Foundation of China
2023.01—2026.12, Member, Nonhomogeneous Hidden Markov Models with Massive Behavioral Sequences: Modeling, Variational Bayesian Inference and Applications in Mobile Marketing, National Natural Science Foundation of China
2023.01—2025.12, Principal Investigator, High-dimensional Conditional Heteroskedasticity Models: Statistical Inference and Applications, National Natural Science Foundation of China
2022.04—2025.03, Principal Investigator, Robust Statistical Inference for High Dimensional Change Points, Shanghai Municipal Commission of Science and Technology
2020.01—2023.12, Member, Statistical Inference and Applications for Nonlinear and Nonstationary Time Series Models with Exogeneous Variables, National Natural Science Foundation of China
2018.01—2021.12, Member, Network-Based Analysis of High-Dimensional Time Series Models, National Natural Science Foundation of China
Journal Papers
| 1. | Cheng Yu, Dong Li, Feiyu Jiang, and Ke Zhu. 2025. Matrix GARCH model: Inference and application. Journal of the American Statistical Association 120(551) 1747-1762. |
| 2. | Kunyang Song, Feiyu Jiang, and Ke Zhu. 2025. Estimation for conditional moment models based on martingale difference divergence. Journal of Time Series Analysis 46(4) 727-747. |
| 3. | Rohit Kanrar, Feiyu Jiang, Zhanrui Cai. 2025. Model-free change-point detection using AUC of a classifier. Journal of Machine Learning Research 26(190) 1-50. |
| 4. | Feiyu Jiang, Hanjia Gao, and Xiaofeng Shao. 2024. Testing serial independence of object-valued time series. Biometrika 111(3) 925–944. |
| 5. | Feiyu Jiang, Changbo Zhu, and Xiaofeng Shao. 2024. Two-sample and change-point inference for non-Euclidean valued time series. Electronic Journal of Statistics 18(1) 848-894. |
| 6. | Feiyu Jiang, Dong Li, Wai Keung Li, and Ke Zhu. 2023. Testing and modelling for the structural change in covariance matrix time series with multiplicative form. Statistica Sinica 33(2) 787-818. |
| 7. | Feiyu Jiang, Runmin Wang, and Xiaofeng Shao. 2023. Robust inference for change points in high dimension. Journal of Multivariate Analysis 193 1-23. |
| 8. | Feiyu Jiang, Zifeng Zhao, and Xiaofeng Shao. 2023. Time series analysis of COVID-19 infection curve: A change-point perspective. Journal of Econometrics 232(1) 1-17. |
| 9. | Feiyu Jiang, Zifeng Zhao, and Xiaofeng Shao. 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. |
| 10. | Zifeng Zhao, Feiyu Jiang, and Xiaofeng Shao. 2022. Segmenting time series via self-normalisation. Journal of the Royal Statistical Society Series B-Statistical Methodology 84(5) 1699–1725. |
| 11. | Jiayuan Zhou, Feiyu Jiang, Ke Zhu, and Wai Keung Li. 2022. Time series models for realized covariance matrices based on the matrix-f distribution. Statistica Sinica 32(2) 755-786. |
| 12. | Feiyu Jiang, Dong Li, and Ke Zhu. 2021. Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model. Journal of Econometrics 224(2) 306-329. |
| 13. | Youhong Ben and Feiyu Jiang. 2020. A note on Portmanteau tests for conditional heteroscedastistic models. Economics Letters 192 1-5. |
| 14. | Feiyu Jiang ,Dong Li, and Ke Zhu. 2020. Non-standard inference for augmented double autoregressive models with null volatility coefficients. Journal of Econometrics 215(1) 165-183. |
Research Projects
2025.01—2029.12, Member, Research on Innovation and Application of AI Marketing Technology: The Perspective of Creation Capability of AI, National Natural Science Foundation of China
2024.01—2028.12, Member, Statistical Inference of Multi-source Heterogeneous Data based on Information Fusion and Migration, National Natural Science Foundation of China
2023.01—2026.12, Member, Nonhomogeneous Hidden Markov Models with Massive Behavioral Sequences: Modeling, Variational Bayesian Inference and Applications in Mobile Marketing, National Natural Science Foundation of China
2023.01—2025.12, Principal Investigator, High-dimensional Conditional Heteroskedasticity Models: Statistical Inference and Applications, National Natural Science Foundation of China
2022.04—2025.03, Principal Investigator, Robust Statistical Inference for High Dimensional Change Points, Shanghai Municipal Commission of Science and Technology
2020.01—2023.12, Member, Statistical Inference and Applications for Nonlinear and Nonstationary Time Series Models with Exogeneous Variables, National Natural Science Foundation of China
2018.01—2021.12, Member, Network-Based Analysis of High-Dimensional Time Series Models, National Natural Science Foundation of China
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