刘婧媛

教授

美国宾夕法尼亚州立大学 统计学博士

电话:0592-2580657

电子邮件:jingyuan@xmu.edu.cn

办公室:经济楼D307

Office Hours:

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个人简介 研究成果 研究项目

刘婧媛,女,1986年12月生,山东青岛人,统计学博士。厦门大学经济学院统计系、王亚南经济研究院教授,博士生导师,教育部青年长江学者,厦门大学南强青年拔尖人才(A类)。科研方面主要从事高维数据的统计分析方法、因果推断、交叉学科中统计方法的研究等领域的工作,在 Journal of American Statistical Association (JASA), Journal of Econometrics (JOE), Journal of Business & Economic Statistics (JBES),Annals of Applied Statistics (AOAS)等国际权威学术期刊发表论文20余篇;主持国家自然科学基金、科技部重点研发计划子课题等国家级、省部级多项科研项目;担任AOAS编委。教学方面曾获厦门大学教学技能大赛暨英语教学比赛特等奖、宾夕法尼亚州立大学最佳教学奖等荣誉;参与编著《数据思维:从数据分析到商业价值》、《数据思维实践:从零经验到数据英才》等教材。
 
工作经历
2020至今,厦门大学经济学院统计系及王亚南经济研究院,教授(破格晋升)
2016-2020,厦门大学经济学院统计系及王亚南经济研究院,副教授
2013-2016,厦门大学经济学院统计系及王亚南经济研究院,助理教授
2012-2012,美国Amgen制药公司,数据分析师(实习)
2011-2012,美国宾夕法尼亚州立大学统计咨询中心,统计咨询员
 
教育经历
2008-2013,美国宾夕法尼亚州立大学统计学专业,博士
2004-2008,山东大学数学与系统科学学院统计学专业,本科

 

部分发表论文:(#:联合第一作者,*:通讯作者) 

1)       Liu J., Sun A. and Ke Y. (2022+). A Generalized Knockoff Procedure for FDR Control in Structural Change Detection. Journal of Econometrics. In press. DOI: 10.1016/j.jeconom.2022.07.008.

2)       Guo X.#, Li R.#, Liu J.*# and Zeng M.# (2022+). Statistical Inference for Linear Mediation Models with High-dimensional Mediators and Application to Studying Stock Reaction to COVID-19 Pandemic. Journal of Econometrics. In press. DOI: 10.1016/j.jeconom.2022.03.001.

3)       Liao Y.#, Liu J.*#, Coffman D. L.# and Li R.# (2022). Varying Coefficient Mediation Model and Application to Analysis of Behavioral Economics Data. Journal of Business & Economic Statistics. 40(4), 1759-1771.

4)       Guo X.#, Li R.#, Liu J.*# and Zeng M.# (2022). High-dimensional Mediation Analysis for Selecting DNA Methylation Loci Mediating Childhood Trauma and Cortisol Stress Reactivity. Journal of the American Statistical Association. 117(539), 1110-1121. 

5)       Liu W.#, Ke Y.*#, Liu J.*# and Li R.# (2022). Model free Feature Screening and FDR Control with Knockoff Features. Journal of the American Statistical Association. 117(537), 428-443

6)       Wang F., Liu J.* and Wang H. (2021). Sequential Text-Term Selection in Vector Space Models. Journal of Business & Economic Statistics. 39(1), 82-97. 

7)       Chu W.#, Li R.#, Liu J.*# and Reimherr M.# (2020). Feature selection for generalized varying coefficient mixed-effect models with application to obesity GWAS. Annals of Applied Statistics. 14(1), 276–298. 

8)       Zhou Y.#, Liu J.# and Zhu L. (2020). Test for Conditional Independence with Application to Conditional Screening. Journal of Multivariate Analysis. 175.

9)       Liu J.*, Legg J., Mo. M and Zhang X. (2019). Considerations in testing treatment effects on transient event driven health status changes measured by patient reported outcomes. Statistics in Medicine. 38(29), 5497-5511.

10)     Zhou Y., Liu J.*, Hao Z. and Zhu L. (2019). Model-Free Conditional Feature Screening with Exposure Variables. Statistics and Its Interface. 12, 239-251. 

11)     Liu J., Lou L. and Li R. (2018). Variable Selection for Partially Linear Models via Partial Correlation. Journal of Multivariate Analysis. 167, 418–434. 

12)     Liu J.*, Ye M., Zhu S., Jiang L., Sang M., Gan J., Wang Q., Huang M. and Wu R. (2018). Two-stage Identification of SNP Effects on Dynamic Poplar Growth. The Plant Journal, 93, 286–296.

13)     Li R.#, Liu J.*# and Lou L.# (2017). Variable Selection via Partial Correlation. Statistica Sinica. 27 (3), 983-996. 

14)     Wang L., Liu J.*, Li Y. and Li R. (2017). Model-Free Conditional Independence Feature Screening. Science China Mathematics. 60(3), 551-568. 

15)     Liu J.* (2016). Feature Screening and Variable Selection for Partially Linear Models with Ultrahigh- dimensional Longitudinal Data. Neurocomputing. 195, 202-210. 

16)     Shi L., He Q., Liu J. and He Z. (2016). A Modified Region Approach for Multivariate Measurement System Capability Analysis. Quality and Reliability Engineering International. 32(1), 37-50.

17)     Liu J., Zhong W. and Li R. (2015). A Selective Overview of Feature Screening for Ultrahigh-dimensional Data. Science China Mathematics, 58(10), 1-22. 

18)     Jiang L.#, Liu J.#, Zhu X., Ye M., Sun L., Lacaze X. and Wu R. (2015). 2HiGWAS: A Unifying High-Dimensional Platform to Infer the Global Genetic Architecture of Trait Development. Briefings in Bioinformatics, 16(6), 905-911. 

19)     Liu J.*, Li R. and Wu R. (2014). Feature Selection for Varying Coefficient Models with Ultrahigh Dimensional Covariates. Journal of the American Statistical Association, 109, 266-274. 

20)     Liu J., Wang Z., Wang Y., Li R. and Wu R. (2012). Model and Algorithm for Linkage Disequilibrium Analysis in a Nonequilibrium Population. Frontiers in Statistical Genetics. 3, 78. 

21)     Wang Z., Liu J., Wang J., Wang Y., Wang N., Li Y., Li R. and Wu R. (2012). Dynamic Modeling of Genes Controlling Cancer Stem Cell Proliferation. Frontiers in Statistical Genetics. 3, 84.

22)     Das K., Huang Z., Liu J., Fu G., Li J., Li Y., Tong C., Gai J. and Wu R. (2012). Functional Mapping of Developmental Processes: Theory, Applications, and Prospects. Methods in Molecular Biology. 871, 227-243. 

23)     Li J., Das K., Liu J., Fu G., Li Y., Tobias C. and Wu R. (2012). Statistical Models for Genetic Mapping in Polyploids: Challenges and Opportunities. Methods in Molecular Biology. 871, 245-261. 

24)     Hou W., Sui Y., Wang Z., Wang Y., Wang N., Liu J., Li Y., Goodenow M., Yin L., Wang Z. and Wu R. (2012). Systems mapping of HIV-1 infection. BMC Genetics. 13(1), 91-97. 

 

 

 

 

 

主持科研项目

1)        “错误发现率控制方法的统计理论及应用”,国家自然科学基金面上项目,2023-2026

2)        “结构变化识别的统计建模研究”,教育部人文社科规划基金项目,2022-2025

3)        “高维模型误差项分布的研究与应用”,国家自然科学基金面上项目,2018-2021

4)        “基于偏相关系数截断法的超高维模型的变量选择” 国家自然科学基金青年科学基金项目,2015-2017

5)        “部分线性模型的大数据变量选择两步法”,福建省教育厅社会科学研究项目,2014-2015

6)        “超高维变系数模型及拓展的统计分析方法”,教育部留学回国人员科研启动基金,2014-2016

7)        “含有多重因变量的大数据统计分析方法,及此方法在动态生长曲线基因定位研究中的应用”,中央高校基本科研业务费,2013-2015