周勇

作者: 付鲁菲 时间: 2023-05-09 点击次数: 1178

姓名:周勇
性别:
民族:
办公电话:0532-62231005
职务:华东师范大学统计交叉科学研究院院长
职称:教授
 电子邮箱yzhou@fem.ecnu.edu.cn

个人简介:周勇,博士,国务院学位委员会第七届统计学科评议组成员,国务院政府特殊津贴专家,新世纪百千万人才国家级人选,国家级专家,国家自然科学基金委杰出青年基金获得者,教育部应用统计专业硕士教学指导委员会委员,中国优选法统筹法与经济数学研究会副理事长(2014--),全国工业统计教育学会副会长(2018--2022),管理科学与工程学会常务理事(2018-2022)。曾任中国现场统计研究会环境与资源统计分会理事长(2007-2017),中国统计教育学会副会长(2015---2019)19857月毕业于华中师范大学数学系,获得理学学士学位;19887月毕业于中国科技大学数学系,获得理学硕士学位;19943月毕业于中国科学院应用数学研究所,获得理学博士。1996年毕业于北京大学概率统计系,获得理学博士后学位。

学术兼职:《数理统计与管理》编委、《应用数学学报》执行编委、中国科学院数学与系统科学研究院生物信息中心副主任、中国科学院数学与系统科学研究院金融工程与风险管理研究中心第一批特聘研究人员、美国《数学评论》(Mathematical Review)特聘评论员、国家基金委自然科学基金项目评议专家、中国科学院数学与系统科学研究院应用数学所创新团队复杂数据统计与金融的组员、中国现场统计学会环境与资源统计研究会理事长,中国现场统计学会理事,概率统计学会常务理事,生存分析分会理事、中国统计学会会员,中国数学学会会员,中国现场统计学会会员 、《数理统计与管理》志审稿人。

通讯地址:山东省青岛市黄岛区长江西路66号工科E1117
研究领域

图形图像处理、机器学习、知识工程

主讲课程

《无线传感网络》、《过程控制仪表与装置》

教学、科研项目

1. 留学回国自然科学基金项目:“复杂多元删失纵向数据统计分析”,(编号:K690011G212005-2006年,项目负责人。

2. 国家自然科学海外杰出青年基金项目:“金融工程中统计模型的若干前沿问题研究”(编号:10628104),2006-2010年,国内合作人。

3. 国家重点基础研究发展计划(973计划)项目“金融风险控制中的定量分析与计算”子项目: “金融创新产品的设计和定价” (编号:2007CB814902),2007-2012年,参与。

4. 国家863重大项目“转化医学相关信息的整合与利用”(编号:SQ2007AA02Z336549)子项目“药物临床活性和药代特征的早期评价和预测网络计算系统”,2007-2011年,参与。

5. 国家基金委自然科学基金重点项目“复杂数据的统计分析及其应用” (编号:107310102007-2011年,协作单位项目负责人。

6. 国家自然科学基金委创新研究群体科学基金“随机复杂数据与随机复杂结构的理论方法及其应用”(编号:10721101)2007-2013年,参与。

7. 国家杰出青年基金“风险计量经济模型的建模、预测及应用” (编号:70825004),2008-2012年,项目负责人。

8. 中国科学院科学出版基金的项目:“一般估计方程统计方法”,2008-2009年,项目负责人。

9. 国家自然科学基金委员会(NSFC)与英国爱丁堡皇家学会(RSE)在管理科学(Management Sciences)领域共同资助两国科学家的合作研究项目,“复杂经济系统中参数与非参数估计”(编号:70911130018),2009-2011年,项目负责人。

10. 国家自然科学基金委基金项目“复杂因素下金融风险度量与风险传染建模与风险管理” (编号:712711282012-2017年,负责人。

11. 国家自然科学基金委重点项目“复杂环境下资产定价与风险管理的金融计量理论及其应用” (编号:71331006 2013-2018年,项目负责人。

12. 国家自然科学基金委重大研究计划重点项目“金融大数据统计学习理论与方法及其在互联网金融中的应用” (编号:91546202 2016-2020年,项目负责人。

13. 国家自然科学基金重点项目“经济管理中复杂数据和复杂行为的分析方法及其应用”(编号:71931004 2019-2024年, 项目负责人。

14. 国家自然科学基金委重大研究计划“大数据驱动下的管理与决策”培育项目“面向大数据的统计分布式计算及隐私保护的理论与方法”(编号:92046005),2021-2022年,项目负责人 

15. 国家重点研发计划“油气管网安全运维的大数据分析理论、算法及应用”(批准号:2021YFA10001002021YFA1000101),2021.12-2026.11,项目负责人

论文论著

(1) Zheng, S.M. Qin, J. and Zhou, Y. (2021). Effect assessment of age and gender on the incubation period of COVID-19 with mixture regression model. Journal of Data Science. To appear.

(2) Li, P. Song, S.S. and Zhou, Y. (2021). Semiparametric additive frailty hazard model for clustered failure time data. Canadian Journal of Statistics. To appear.

(3) Fan, C.Y. Lu,W.B. and Zhou, Y. (2021). Testing error heterogeneity in censored linear regression.Computational Statistics and Data Analysis, 161,107207.

(4) Jiang, Z.F. Yang, B.Y. Qin, J. and Zhou, Y. (2021). Enhanced empirical likelihood estimation of incubation period of COVID-19 by integrating published information. Statistics in Medicine.404252-4268.

(5) Xun, L. Zhang, G.C.Wang, D.H. and Zhou, Y. (2021) Estimating equation estimators of quantile differences for one sample with length-biased and right-censored data. Statistics and Its Interface,14, 183-195.

(6) Ma, H.J. Zhao,W. and Zhou, Y. *(2020). Semiparametric model of mean residual life with biased sampling data. Computational Statistics and Data Analysis, 142:106826.

(7) Song, X.Y., Kim D., Yuan, H.L., Cui, X.Y. Lu, Z.P. Zhou, Y. andWang, Y. (2020). Volatility analysis with realized GARCH-Ito models. Journal of Econometrics, 26,761-788.

(8) Chen, L.J. and Zhou, Y. (2020). Quantile regression in big data: a divide and conquer based strategy. Computational Statistics and Data Analysis, 142: 106892.

(9) He, Y. F. and Zhou, Y. (2020). Nonparametric and semiparametric estimators of restricted mean survival time under length-biased sampling. Accepted in Lifetime Data Analysis.

(10) Xu, D.* and Zhou, Y. (2020). Local composite partial likelihood estimation for length-biased and right-censored data. Journal of Statistical Computation and Simulation, 89 (14), 2661C2667.

(11) Xun, L., Li, T. and Zhou, Y. (2020). Estimators of quantile difference between two samples with length-biased and right-censored data. Test, 29, 409-429.

12) Liu, Y. T., Zhang, S. C. and Zhou, Y. Semiparametric quantile difference estimation for lengthbiased and right-censored data. Science in China, Mathematics. 62: 1823-1838.

(13) Chen, X.*, Chen, Y., Wan, A. and Zhou, Y. (2019). On the asymptotic non-equivalence of efficient-GMM and MEL estimators in models with missing data. Scandinavian Journal of Statistics,46(2),361-368

(14) Pan, J., Zhang, S. and Zhou, Y. (2019) Variable screening for ultrahigh dimensional censored quantile regression. Journal of Statistical Computation and Simulation, 89 (3), 395C413.

(15) Xun, L., and Zhou, Y. (2019) A generalization of Expected Shortfall based capital allocation.Statistics and Probability Letters, 146, 193-199.

(16) Zhang, F., Peng, H. and Zhou, Y. (2019) Fine-Gray proportional subdistribution hazards model for competing risks data under length-biased sampling. Statistics and its Interface, 12 (1), 107-122.

(17) Wei,W., Zhou, Y. andWan, Alan T. K. (2019) A semiparametric linear transformation model for general biased-sampling and right-censored data. Statistics and its Interface, 12(1), 77-92.

(18) Liu, Y., Lin, C. and Zhou, Y. (2019) Nonparametric estimates of conditional quantile residual lifetime for right censored data. Statistics and its Interface, 12 (1), 61-70

(19) Ma, H., Shi, J. and Zhou, Y. (2019) Proportional mean residual life model with censored survival data under case-cohort design. Statistics and its Interface, 12 (1), 21-33.

(20) Wang, X., Zhou, Y. and Liu, Y. (2019) Semiparametric varying-coefficient partially linear models with auxiliary covariates. Statistics and its Interface, 11 (4), 587-602.

(21) Qiu, Z. P., Wan Alan, T. K.*, Zhou, Y. and Gilbert, P. (2019). Smoothed rank regression for the accelerated failure time competing risks model with missing cause of failure. Statistica Sinica,29, 23-46.

(22) Liu, X.Q., Song, X. Y. and Zhou, Y. (2019). Likelihood ratio-type tests in weighted composite quantile regression of DTARCH models. Science China Mathematics, 62(12), 2571-2590.

(23) Wei, W. H. Wan, T. K. and Zhou, Y. (2018). Partially linear transformation model for lengthbiased and right-censored data. Journal of Nonparametric Statistics, 30(2), 332-367.

(24) He, D., Zhou, Y., and Zou, H. (2018). High-dimensional variable selection with right censored length-biased data. Statistica Sinica, DOI: 10.5705/ss.202018.0089.

(25) Lin, C. J., Wei, W. H. * and Zhou, Y. (2018). Semiparametric estimation of treatment effect with density ratio model. Communications in Statistics-Theory and Methods, 14, 3338-3359.

(26) Zhang, L. Lin, C. and Zhou, Y. (2018). Generalized method of moments for nonignorable missing data. Statistica Sinica 28, 2107-2124

(27) Fan, C., Ma, H. * and Zhou, Y. (2018). Quantile regression for competing risks analysis under case-cohort design. Journal of Statistical Computation and Simulation, 88(6), 1060-1080.

(28) Zhang, S. * and Zhou, Y. (2018). Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations. Journal of Multivariate Analysis, 165, 1-13.

(29) Liu, Y., Zhang, S. * and Zhou, Y. (2018). Semiparametric estimation of quantile differences for length-biased and right-censored data. Science in China Mathematics, to appear.

(30) Pan, J., Yu, Y., and Zhou, Y. (2018). Nonparametric independence feature screening for ultrahigh dimensional survival data. Metrika, 81, 821-847.

(31) Yu, Y. He, D. and Zhou, Y. (2018). Robust model-free feature screening based on modified Hoeffding measure for ultra-high dimensional data, Statistics and Its Interface, 11, 473-489.

(32) Zhang, F.*, Zhao. X. and Zhou, Y. (2018). An embedded estimating equation for additive risk model with biased-sampling data. Science in China, Mathematics, 61(8), 1495-1518.

(33) Wang, X. J.*, Zhou, Y. and Liu, Y. (2018). Semiparametric varying-coefficient partially linear model with auxiliary covariates. Statistics and Its Interface, 11587-602.

(34) Zhang, F. and Zhou, Y. * (2018). Nonparametric quantile estimate for length-biased and right censored data with competing risks. Communications in Statistics-Theory and Methods, 10, 2407-2424

(35) Zhang S. Z. Pan, J. and Zhou, Y. (2018). Robust conditional nonparametric independence screening for ultrahigh-dimensional data. Statistics and Probability Letters, 143, 95-101.

(36) Shi, J. H.*, Ma, H. J. and Zhou, Y. (2018). The nonparametric quantile estimation for lengthbiased and right-censored data. Statistics and probability Letter, 134, 150-158.

(37) Fan, C., Ma, H.* and Zhou, Y. (2018). Quantile regression for competing risks analysis under case-cohort design. Journal of Statistical Computation and Simulation, 88(6), 1060-1080.

(38) Zhang, S.* and Zhou, Y. (2018). Variable screening for ultrahigh dimensional heterogeneous data via conditional quantile correlations. Journal of Multivariate Analysis, 165, 1-13.

(39) Zhang, F. and Zhou, Y. * (2018). Nonparametric quantile estimate for length-biased and rightcensored data with competing risks. Communications in Statistics-Theory and Methods, 10, 2407-2424

获得荣誉

1. 湖南省优秀科技成果三等奖(1990年);

2. 中国科学院应用数学研究所优秀博士奖(1994年);

3. 作为主要参加者参加的中国航天工业总公司的项目“系统可靠性增长试验设计与分析”,获航天工业总公司科技进步二等奖(1997年)。

4. 国家统计局优秀科研成果(论文类)二等奖(2012年)

5. 《创新统计人才培养模式的实践与探索》上海市教学成果一等奖(2017年,排名1)

指导学生
2008年以来共指导培养,博士研究生毕业54名,硕士研究生毕业70余名;

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