孔新兵

发布者:吴彤发布时间:2017-03-11浏览次数:10711

 

               

姓名孔新兵

职称教授                                   

研究领域高频数据分析、髙维数据分析、网络数据统计


教育背景

香港科技大学、博士、2007-2011

兰州大学、硕士及本科、2000-2007


工作经历

南京审计大学、理学院/统计与大数据研究院、教授、20173-至今;

苏州大学、数学学院/高等统计与计量经济研究中心、教授、20148-20172月;

复旦大学、管理学院、副教授及助理教授、20117-20147月。


课题主持自然科学基金面上2项、青年项目1项、教育部人文社科项目1项;参与国家自然科学基金重点项目1项。


代表性论文

  1. He Yong, Kong Xin-Bing*, Yu Long, Zhang Xin-Sheng (2020). Large-dimensional Factor Analysis without Moment Constraints. Journal of Business and Economic Statistics, online, https://doi.org/10.1080/07350015.2020.1811101

  2. Xin-Bing Kong. Rank estimator of the number of factors: random perturbation, expansion and central limit theorem, Biometrika, 2019, Accepted.

  3. Kong, X., Wang, J., Xing, J., Xu, C., and Ying, C. Factor and idiosyncratic empirical processes. Journal of American Statistical Association, 2019,DOI:10.1080/01621459.2018.1469997

  4. Xin-Bing Kong, Zhi Liu, Wang Zhou. A rank test for the number of factors with high-frequency data, Journal of Econometrics, 2019, 211(3), 439-460.

  5. Liu Zhi,Kong Xin-Bing*, Jing Bing-Yi,Estimating  the integrated volatility using high-frequency data with zero durations,Journal of Econometrics, 2018,204(1), 18-32.

  6. Kong Xin-Bing, Liu Cheng, Testing against constant factor loading matrix with large panel high-frequency data, Journal of Econometrics, 2018, 204(2), 301-319.

  7. Kim, Donggyu, Kong Xin-Bing, Li, Cuixia, Wang Yazhen, Adaptive thresholding estimator of the large dimensional integrated volatility matrix, Journal of Econometrics, 2018, 203, 69-79.

  8. Kong Xin-Bing, Xu Shao-Jun, Zhou Wang*, Bootstrapping volatility functionals: a local and nonparametric perspective, Biometrika,2018, 105(2), 463-469.

  9. Kong Xin-Bing,On the integrated idiosyncratic and systematic volatility with the large panel high-frequency data, Annals of Statistics, 2018, 46(3), 1077-1108.

  10. Kong Xin-Bing, On the number of common factors underlying large panel high-frequency data, Biometrika,2017, 104, 397-410.

  11. Kong Xin-Bing, Liu Zhi, Jing Bing-Yi, A new test for pure-jump processes underlying high frequency data, Annals of Statistics, 2015, Vol. 43, 847-877.

  12. Jing Bing-Yi, Kong Xin-Bing, Liu Zhi, Modeling high frequency data by pure jump processes? Annals of Statistics, 2012, 40, 759-784.

  13. Jing Bing-Yi, Kong Xin-Bing, Liu Zhi, Per Mykland, On the jump activity index for semimartingales, Journal of Econometrics, 2012, 166, 213-223.

  14. Jing Bing-Yi, Kong Xin-Bing, Liu Zhi, Estimating the jump activity index under noisy observations using high frequency data, Journal of the American Statistical Association, 2011, 106, 558-568.

  15. Jing Bing-Yi, Liu Zhi, Kong Xin-Bing, On the estimation of integrated volatility with jumps and microstructure noise, Journal of Business and Economic Statistics, 2014, 32, 457-467.


工作论文

  

  1. Kong Xin-Bing, Lin Jin-Guan, Liu Cheng, Liu Guang-Ying (2020). Discrepancy between global and local principal component analysis on large-panel high-frequency data. Journal of the American Statistical Assocation, R&R.

  2. Yu Long, He Yong, Kong Xin-Bing*, Zhang Xin-Sheng (2020). Projected Estimation for Large-dimensional Matrix Factor Models. Journal of Econometrics, R&R.



所授课程《多元分析》、《时间序列》、《金融工程导论》、《概率论于数理统计》、《高等数理统计III》。

荣誉

    国际统计协会(ISI)当选会员;

    江苏省“双创博士”;

    江苏省“青蓝工程”中青年学术带头人;

    香港数学会“最佳博士论文奖”;

    复旦大学管理学院年度“青年新星奖”。

    湖北省社科成果二等奖;

    江苏省应用统计学会2019年优秀论文一等奖。

学术兼职 RMTA,《应用概率统计》编委。