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Fang Fang

  • About

    • Department: School of Statistics
    • Gender: male
    • Post:
    • Graduate School: University of Wisconsin - Madison
    • Degree: Ph.D.
    • Academic Credentials:
    • Tel:
    • Email: ffang@sfs.ecnu.edu.cn
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    • Address:
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    WorkExperience

    2007-08 to 2009-12，GE Money, Senior Analyst

    2010-01 to 2013-07，Shanghai Pudong Development Bank, Strategy Analyst

    2013-08 to 2019-12，East China Normal University, School of Statistics, Associate Professor

    2020-01 to present，East China Normal University, School of Statistics, Professor


    Education

    1998-09 to 2002-07，Peking University, School of Mathematics, B.S.

    2002-08 to 2007-07，University of Wisconsin - Madison，Department of Statistics, Ph.D.


    Resume

    Fang Fang is Professor at School of Statistics in East China Normal University and the Deputy Director of Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE. He got his Bachelor and Doctor Degree from math department of Peking University and statistics department of UW - Madison respectively. Before he joined ECNU at 2013, Doctor Fang worked for GE Money and Shanghai Pudong Development Bank. His research interests include missing data, model averaging and mutilsoure data analysis. He has published over 20 papers at SCI journals including The Annals of Statistics, Biometrika and Journal of Econometrics. He is the Asscoiate Editor of Journal of Nonparametric Statistics. 


    Other Appointments

    Journal of Nonparametric Statistics    Associate Editor  2016 to present


    Research Fields

    Missing data, Model Averaging, Multi-Source Data Analysis

    Enrollment and Training

    Course

    Undregradute courses: College Statistics, Statistical Learning

    Gradudate courses: Mathematical Statistics, Linear Models

    Scientific

    [6] NSFC,12071143,Model Averaging Method and Theory of Fragmentary Data, 2021-01 to 2024-12. PI.

    [5] NSFC,11831008,Research of Multi-Source Data：Fusion, Feature Extraction and Analysis Methods, 2019-01 to 2023-12. PI of Subproject.

    [4] NSFC, 11601156, Reserch of Instrumental Method of Nonignorable Missing Data, 2017-01 to 2019-12. PI.

    [3] Shanghai SF, 15ZR1410300, Reserach of Nonignorable Missing Data in Generalized Linear Models, 2015-01 to 2017-12. PI.

    [2] Shanghai Xianshi Technology Limited Company, Marketing Model Development Based on Big Data of Retal Industry, 2017-09 to 2018-03. PI.

    [1] Hangzhou Hao Hao Driving Limited Company, UBI Model Development, 2016-06 to 2017-12. PI.



    Academic Achievements

    [29] Yuan, Chaoxia, Fang, Fang*, and Li, Jialiang. Model averaging for generalized linear models in diverging model spaces with effective model size. Econometric Reviews, Accepted.

    [28] Fang, Fang* and Bao，Shenliao. FragmGAN: Generative adversarial nets for fragmentary data imputation and prediction.  Statistical Theory and Related Fields, Accepted.

    [27] Fang, Fang*, Yuan, Chaoxia, and Tian, Wenling. An asymptotic theory for least squares model averaging with nested models. Econometric Theory, 2023, 39(2), 412-441.  

    [26] Yuan, Chaoxia*, Wu, Yang, and Fang Fang. Model averaging for generalized linear models in fragmentary data prediction.  Statistical Theory and Related Fields, 2022, 6(4), 344-352.  

    [25] Fang Fang* , Yang, Qiwei, and Tian, Wenling. Cross-validation for selecting the penalty factor in least squares model averaging.  Economics Letters, 2022, 217, Article 110683.

    [24] Fang, Fang*, Li, Jialiang,  and Xia, Xiaochao. Semiparametric model averaging prediction for dichotomous response. Journal of Econometrics, 2022, 229, 219-245.

    [23] Yuan, Chaoxia, Fang, Fang, and Lyu Ni*. Mallows model averaging with effective model size in fragmentary data prediction. Computational Statistics & Data Analysis, 2022, 173, Article 107497.  

    [22] Fang, Fang, Zhao, jiwei, Ahmed, Ejaz, and Qu Annie*. A weak-signal-assisted procedure for variable selection and statistical inference with an informative subsample.  Biometrics, 2021, 77, 996-1010.

    [21] Chen, Ji, Shao, Jun, and Fang, Fang*. Instrument search in pseudo likelihood approach for nonignorable nonresponse. Annals of the Insititute of Statistical Mathematics, 2021, 73, 519-533. 

    [20] Wang, Lei, Shao, Jun, and Fang, Fang*. Propensity model selection with nonignorable nonresponse and instrument variable. Statistica Sinica, 2021, 31, 647-672.

    [19] Fang, Fang* and Liu, Minhan. Limit of the optimal weight in least squares model averaging with non-nested models. Economics Letters, 2020, 196, 109586.

    [18] Fang, Fang and Yu, Zhou*. Model averaging assisted sufficient dimension reduction. Computational Statistics & Data Analysis, 2020, 152, Article 106993. 

    [17] Ni, Lyu, Fang, Fang*, and Shao, Jun. Feature screening for ultrahigh dimensional categorical data with covariates missing at random. Computational Statistics & Data Analysis, 2020, 142, Article 106824. 

    [16] Fang, Fang*, Lan, Wei, Tong, Jingjing, and Shao, Jun. Model averaging for prediction with fragmentary data. Journal of Business & Economic Statistics. 2019, 37, 517-527

    [15] Fang, Fang, Wang, Jingli, and Li, Jialiang*. Optimal model averaging estimation for correlation structure in generalized estimating equations. Communications in Statistics - Simulation and Computation. 2019, 48, 1574-1593.

    [14]  Chen, Ji and Fang, Fang*. Semiparametric likelihood for estimating equations with nonignorable nonresponse by nonresponse instrument.  Journal of Nonparametric Statistics.  2019, 31, 420-434.

    [13] Fang, Fang* and Chen, Yuanyuan. A new approach for credit scoring by directly maximizing the Kolmogorov-Smirnov statistic.  Computational Statistics & Data Analysis.  2019, 133, 180-194. 

    [12] Fang, Fang*, Yin, Xiangju, and Zhang, Qiang. Divide and conquer algorithms for model averaging with massive data. Journal of System Science and Mathematical Sciences, Chinese Series. 2018, 38, 764-776. An invited paper for the special issue of model averaging.

    [11] Fang, Fang*, and Ni, Lyu. Variable screening with missing covariates: A discussion of Statistical inference for nonignorable missing data problems: A selective review by Niansheng Tang and Yuanyuan Ju. Statistical Theory and Related Fields, 2018, 2, 134-136. 

    [10] Fang, Fang, Zhao, Jiwei, and Shao, Jun*. Imputation-based adjusted score equations in generalized linear models with nonignorable missing covariate values. Statistica Sinica, 2018, 28, 1677-1701.

    [9] Chen, Ji, Fang, Fang and Xiao, Zhiguo*,  Semiparametric inference for estimating equations with nonignorable missing covaraites. Journal of Nonparametric Statistics, 2018, 30, 796-812.

    [8] Ni, Lyu, Fang, Fang*, and Wan, Fangjiao. Adjusted Pearson Chi-Square feature screening for multi-classification with ultrahigh dimensional data. Metrika, 2017, 80, 805-828. 

    [7] Fang, Fang, and Shao, Jun*.  Model selection with nonignorable nonresponse. Biometrika, 2016, 103, 861-874. 

    [6] Fang, Fang*, Fan, Xiaoyin, and Zhang Ying.   Estimation of response from longitudinal binary data with noignorable missing values in migraine trails.  Contemporary Clinical Trials Communications, 2016, 4, 90-98.

    [5] Fang, Fang, and Shao, Jun*.  Iterated imputation estimation for generalized linear models with missing response and covariate values.  Computational Statistics & Data Analysis, 2016, 103, 111-123.

    [4] Ni, Lyu, and Fang, Fang*.  Entropy based model free feature screening for ultrahigh dimenisonal multiclass classification. Journal of Nonparametric Statistics, 2016, 28, 515-530.

    [3] Fang, Fang*.  Regression analysis with nonignorably missing covariates using surrogate data. Statistics and Its Interface, 2016, 9, 123-130.

    [2] Fang, Fang, Hong, Quan, and Shao, Jun*. Empirical likelihood estimation for samples with nonignorable nonresponse. Statistica Sinica, 2010, 20, 263-280.

    [1] Fang, Fang, Hong, Quan, and Shao, Jun*. A pseudo empirical likelihood approach for stratified samples with nonresponse. The Annals of Statistics, 2009, 37, 371-393 .



    Honor

    1、“Study on Several Types of Complex Data Analysis”, The Scientific Award of Shanghai, 2nd Prize, 2019.