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數(shù)字引領(lǐng)時(shí)代　　智能開(kāi)創(chuàng)未來(lái)

姜榮（JIANG Rong）

教授電話(huà)：
 電子郵件：[email protected]

教育背景

博士（應(yīng)用數(shù)學(xué)），2014，同濟(jì)大學(xué)
學(xué)士（統(tǒng)計(jì)學(xué)），2009，同濟(jì)大學(xué)

研究領(lǐng)域

大數(shù)據(jù)統(tǒng)計(jì)、在線(xiàn)數(shù)據(jù)統(tǒng)計(jì)分析、分布式計(jì)算、分位數(shù)回歸、非參數(shù)/半?yún)?shù)模型

主講課程

《金融數(shù)據(jù)風(fēng)險(xiǎn)建?！?/p>

簡(jiǎn)介

上海對(duì)外經(jīng)貿(mào)大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)學(xué)院教授，碩士生導(dǎo)師。2014年在東華大學(xué)統(tǒng)計(jì)系任教，2017-2018年在英國(guó)布魯奈爾大學(xué)做訪(fǎng)問(wèn)學(xué)者，2024年11月進(jìn)入上海對(duì)外經(jīng)貿(mào)大學(xué)統(tǒng)計(jì)與信息學(xué)院任教。擔(dān)任全國(guó)工業(yè)統(tǒng)計(jì)學(xué)教學(xué)研究會(huì)金融科技與大數(shù)據(jù)技術(shù)分會(huì)理事。主要從事大數(shù)據(jù)建模、分位數(shù)回歸和在風(fēng)險(xiǎn)管理中的應(yīng)用研究。在統(tǒng)計(jì)學(xué)國(guó)際著名期刊《Journal of the Royal Statistical Society: Series B》《Journal of Business & Economic Statistics》《Journal of Financial Econometrics》《Test》《Journal of Multivariate Analysis》等發(fā)表SCI和SSCI論文30余篇。主持國(guó)家自然科學(xué)基金青年基金、國(guó)家自然科學(xué)基金天元基金、教育部人文社科基金和上海市揚(yáng)帆計(jì)劃。


部分發(fā)表論文

  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. 姜榮, 錢(qián)偉民, 周占功. (2011). 半?yún)?shù)測(cè)量誤差模型中參數(shù)的隨機(jī)加權(quán)估計(jì). 同濟(jì)大學(xué)學(xué)報(bào)(自然科學(xué)版), 39(5).

 

科研項(xiàng)目

  1. 教育部人文社會(huì)科學(xué)研究青年基金項(xiàng)目，高維流數(shù)據(jù)下線(xiàn)性分位數(shù)回歸模型的理論研究及應(yīng)用，2022.09-2024.10，主持。

  2. 國(guó)家自然科學(xué)基金面上項(xiàng)目，多響應(yīng)線(xiàn)性模型實(shí)驗(yàn)設(shè)計(jì)的容許性、不變性和幾何刻畫(huà)，2019.01-2022.12，參與。

  3. 國(guó)家自然科學(xué)基金青年基金項(xiàng)目，大數(shù)據(jù)下單指標(biāo)模型的統(tǒng)計(jì)推斷研究，2019.01-2021.12，主持。

  4. 上海市揚(yáng)帆計(jì)劃，超高維數(shù)據(jù)單指標(biāo)模型的變量選擇問(wèn)題研究，2017.05-2020.04，主持。

  5. 國(guó)家自然科學(xué)基金天元基金項(xiàng)目，單指標(biāo)模型估計(jì)方法的研究，2017.01-2017.12，主持。