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    Dai Guorong

    Assistant Professor

    Statistics and Data Science

    Add: Guoshun Road Siyuan Building Room 713, Siyuan Building

    Tel:

    E-mail: guorongdai@fudan.edu.cn

    Research Field: Missing Data, Quantile Regression, Semi-Supervised Inference, Variable Selection

    Website:

    Teacher information
    Achievements in scientific research
    Academic activities and community service

    Educational Background

    Doctor Degree,Statistics,Texas A&M University, USA

    Bachelor Degree,Statistics,Renmin University of China

    Awards on Teaching

    2023.12,The Second Prize in Fudan University 2023 Young Teacher Teaching Competition in Social Science Group,Fudan University


    Journal Papers


    1.Guorong Dai, Raymond J. Carroll, and Jinbo Chen. 2025. Valid and efficient inference for nonparametric variable importance in two-phase studies. Biometrics 81(3) 1-11.  



    2.Guorong Dai, Lingxuan Shao, and Jinbo Chen. 2025. Moving beyond population variable importance: concept, theory and applications of individual variable importance. Journal of the Royal Statistical Society Series B-Statistical Methodology 87(3) 816–832.  



    3.Guorong Dai, Ursula U. Müller, and Raymond J. Carroll. 2024. Penalized regression with multiple loss functions and variable selection by voting. Statistica Sinica forthcoming 1-41.  



    4.Guorong Dai and Jinbo Chen. 2024. Robust and efficient case-control studies with contaminated case pools: A unified M-estimation framework. Statistica Sinica forthcoming 1-41.  



    5.Guorong Dai, Yanyuan Ma, Jill Hasler, Jinbo Chen, and Raymond J. Carroll. 2023. A robust approach for electronic health record-based case-control studies with contaminated case pools. Biometrics 79(3) 2023–2035.  



    6.Guorong Dai, Ursula Müller, and Raymond James Carroll. 2023. Data integration in high dimension with multiple quantiles. Statistica Sinica 33(1) 169-191.  



    7.Guorong Dai and Ursula U. Müller. 2019. Efficient estimators for expectations in nonlinear parametric regression models with responses missing at random. Electronic Journal of Statistics 13(2) 3985-4014.  


    Research Projects

    2025.01—2027.12, Principal Investigator, Semi-Supervised Inference for Treatment Effects in High Dimensions, National Natural Science Foundation of China