数字引领时代　　智能开创未来

姜荣（JIANG Rong）

教授电话：
 电子邮件：jiangrong@suibe.edu.cn

教育背景

博士（应用数学），2014，同济大学
学士（统计学），2009，同济大学

研究领域

大数据统计、在线数据统计分析、分布式计算、分位数回归、非参数/半参数模型

主讲课程

《金融数据风险建模》

简介

上海对外经贸大学统计与信息学院教授，硕士生导师。2014年在东华大学统计系任教，2017-2018年在英国布鲁奈尔大学做访问学者，2024年11月进入上海对外经贸大学统计与信息学院任教。担任全国工业统计学教学研究会金融科技与大数据技术分会理事。主要从事大数据建模、分位数回归和在风险管理中的应用研究。在统计学国际著名期刊《Journal of the Royal Statistical Society: Series B》《Journal of Business & Economic Statistics》《Journal of Financial Econometrics》《Test》《Journal of Multivariate Analysis》等发表SCI和SSCI论文30余篇。主持国家自然科学基金青年基金、国家自然科学基金天元基金、教育部人文社科基金和上海市扬帆计划。


部分发表论文

  1. Jiang R, Yu K. (2024). Rong Jiang and Keming Yu's Discussion of “Estimating means of bounded random variables by betting”. Journal of the Royal Statistical Society: Series B, 86: 38-39.

  2. Jiang R, Yu K. (2024). Unconditional quantile regression for streaming data sets. Journal of Business & Economic Statistics, 42: 1143-1154.

  3. Jiang R, Liang L, Yu K. (2024). Renewable Huber estimation method for streaming datasets. Electronic Journal of Statistics, 18: 674-705. 

  4. Jiang R, Choy S, Yu K. (2024). Non-crossing quantile double-autoregression for the analysis of streaming time series data. Journal of Time Series Analysis, 45: 513-532.

  5. Jiang R, Zhao Y. (2024). Online updating mode learning for streaming datasets. Journal of Statistical Computation and Simulation, 94: 2697-2709.

  6. Jiang R, Yu K. (2023). No-crossing single-index quantile regression curve estimation. Journal of Business & Economic Statistics, 41: 309-320.

  7. Jiang R, Chen S, Wang F. (2023). Quantile regression for massive data set. Communications in Statistics-Simulation and Computation. DOI: 10.1080/03610918.2023.2202840.

  8. Jiang R, Hu X, Yu K. (2022). Single-index expectile models for estimating conditional value at risk and expected shortfall. Journal of Financial Econometrics, 20: 345-366.

  9. Jiang R, Yu K (2022). Renewable quantile regression for streaming data sets. Neurocomputing, 508: 208-224. 

  10. Jiang R, Sun M. (2022). Single-index composite quantile regression for ultra-high-dimensional data. Test, 31: 443-460. 

  11. Jiang R, Guo M, Liu X. (2022). Composite quasi-likelihood for single-index models with massive datasets. Communications in Statistics-Simulation and Computation, 51: 5024-5040.

  12. Jiang R, Yu K. (2021). Smoothing quantile regression for a distributed system. Neurocomputing, 466: 311-326. 

  13. Jiang R, Chen W, Liu X. (2021). Adaptive quantile regressions for massive datasets. Statistical Papers, 62: 1981-1995.

  14. Jiang R, Peng Y, Deng Y. (2021). Variable selection and debiased estimation for single-index expectile model. Australian & New Zealand Journal of Statistics，63: 658-673.

  15. Jiang R, Yu K. (2020). Single-index composite quantile regression for massive data. Journal of Multivariate Analysis, 180: 104669.

  16. Jiang R, Hu X, Yu K and Qian W. (2018). Composite quantile regression for massive datasets. Statistics, 52: 980-1004.

  17. Jiang R, Qian W, and Zhou Z. (2018). Weighted composite quantile regression for partially linear varying coefficient models. Communications in Statistics—Theory and Methods, 47: 3987-4005.

  18. Jiang R, Qian W, Zhou Z.(2016). Weighted composite quantile regression for single-index models. Journal of Multivariate Analysis, 148: 34-48.

  19. Jiang R, Qian W, Zhou Z.(2016). Single-index composite quantile regression with heteroscedasticity and general error distributions. Statistical Papers, 57: 185-203.

  20. Jiang R, Qian W.(2016). Quantile regression for single-index-coefficient. Statistics and Probability Letters, 110: 305-317.

  21. Jiang R.(2015). Composite quantile regression for linear errors-in-variables models. Hacettepe Journal of Mathematics and Statistics, 44: 707-713.

  22. Jiang R, Zhou Z, Qian W.(2015). Generalized analysis-of-variance-type test for the single-index quantile model. Communications in Statistics—Theory and Methods, 44: 2842-2861.

  23. Yang X, Jiang R and Qian W.(2015). Randomly weighted LAD-estimation for partially linear errors-in-variables models. Chinese Annals of Mathematics(Series B), 36：561-578.

  24. Jiang R, Qian W, Zhou Z.(2014). Test for single-index composite quantile regression. Hacettepe Journal of Mathematics and Statistics, 43: 861-871.

  25. Jiang R, Qian W, Li J.(2014). Testing in linear composite quantile regression models. Computational Statistics, 29: 1381-1402.

  26. Jiang R, Zhou Z, Qian W. and Chen Y.(2013). Two step composite quantile regression for single-index models. Computational Statistics & Data Analysis, 64, 180-191.

  27. Zhou Z, Jiang R and Qian W.(2013). LAD variable selection for linear models with randomly censored data. Metrika, 76: 287-300.

  28. Jiang R, Qian W, Zhou Z.(2012). Variable selection and coefficient estimation via composite quantile regression with randomly censored data. Statistics and Probability Letters, 82: 308-317.

  29. Jiang R, Zhou Z, Qian W, Shao W.(2012). Single-index composite quantile regression. Journal of the Korean Statistical Society, 41: 323-332.

  30. Jiang R, Yang X, Qian W.(2012). Random weighting M-estimation for linear errors-in-variables models. Journal of the Korean Statistical Society, 41: 505-514.

  31. Zhou Z, Jiang R and Qian W.(2011). Efficient quantile estimation for functional-coefficient partially linear regression models. Chinese Annals of Mathematics (Series B), 12: 729-740.

  32. Zhou Z, Jiang R and Qian W.(2011). Variable selection for additive partially linear models with measurement error. Metrika, 74: 185-202.

  33. 姜荣, 钱伟民, 周占功. (2011). 半参数测量误差模型中参数的随机加权估计. 同济大学学报(自然科学版), 39(5).

 

科研项目

  1. 教育部人文社会科学研究青年基金项目，高维流数据下线性分位数回归模型的理论研究及应用，2022.09-2024.10，主持。

  2. 国家自然科学基金面上项目，多响应线性模型实验设计的容许性、不变性和几何刻画，2019.01-2022.12，参与。

  3. 国家自然科学基金青年基金项目，大数据下单指标模型的统计推断研究，2019.01-2021.12，主持。

  4. 上海市扬帆计划，超高维数据单指标模型的变量选择问题研究，2017.05-2020.04，主持。

  5. 国家自然科学基金天元基金项目，单指标模型估计方法的研究，2017.01-2017.12，主持。

 


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