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周 岭

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  • 周岭 副教授 博士生导师

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  • Email:zhouling@swufe.edu.cn


  • 教育背景:

  • 美国University of Michigan  博士后 统计学 09/2015-09/2018

  • 西南财经大学            博士 经济学 统计理论与方法研究 09/2011-12/2014

  • 四川大学               硕士 数学 概率论与数理统计学 09/2008-07/2011

  • 四川大学               学士 数学 概率论与数理统计学 09/2004-07/2008

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  • 工作经历:

  • 2018.9-至今 副教授 西南财经大学统计学院,统计研究中心


  • 研究兴趣:

  • 非参数理论与方法、 转换模型、变量选择与高维数据分析、协方差估计,纵向数据分析、大数据整合,亚组分析


  • 学术荣誉:

  • 钟家庆数学奖,2017

  • 四川省特聘专家,2019


  • 科研项目:

  • 2020.1-2022.12,国家自然科学基金青年项目,主持,高维媒介变量半参数多层学习。项目编号:11901470.

  • 2020.1-2024.12,国家自然科学基金重点项目,参与,半参数集成回归推断。项目编号:11931014.

  • 2015.10-2018.9,参加NIH/EPA P01 Children's Environmental Health Center(CEHC) at University of Michigan,"To develop statistical methodologies highly relevant to analytic challenges arising in the analysis of environmental exposures in relation to children's growth and maturation".


  • 研究成果:

  • [1] Luo, L., Zhou, L. and Song, P.X.K. (2022). Real-time regression analysis for streaming clustered data with possible abnormal data batches. Journal of the American Statistical Association (accept)

  • [2] Zhou, L., She, Xi. and Song, P.X.K. (2021+). Distributed empirical likelihood approach to integrating unbalanced data. Statistica Sinica,Doi: 10.5705/ss.202020.0330

  • [3] Zhou, L., Sun, S., Hu, H. and Song, P.X.K. (2021). Subgroup-effects models for the analysis of personal treatment effects. Annals of Applied Statistics, 已接受

  • [4] Xiaoqing Tan, Chung-Chou H. Chang, Ling Zhou, Lu Tang (2022). A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources,Proceedings of the 39 th International Conference on Machine Learning

  • [5] Wang, F., Zhou, L.*, Tang, L. and Song, P.X.K. (2021). Method of contraction-expansion for simultaneous inference in linear models. Journal of Machine Learning Research.(*共同一作), 22(192), 1–32

  • [6] Jansen, E.C., Corcoran, K., Perng, W., Dunietz, G.L., Cantoral, A., Zhou, L., Tellez-Rojo, M.M., and Peterson, K.E. (2021). Relationships of beverage consumption and actigraphy-assessed sleep parameters amog urban-dwelling youth from Mexico. Public Health Nutr. Doi: 10.1017/S136898002100313X.

  • [7] Tang, L., Zhou, L., and Song, P.X.K. (2020). Distributed simultaneous inference in generalized linear models via confidence distribution. Journal of Multivariate Analysis, 176, 104567.

  • [8] LaBarre, J., Puttabyatappa, M., Song, P.X.K., Goodrich, J., Zhou, L., Rajendiran, T., Soni, T., Domino, S., Treadwell, M., Dolinoy, D., Padmanabhan, V., and Burant, C. (2020). Maternal lipid levels across pregnancy impact the umbilical cord blood lipidome and infant birth weight. Scientific Reports, 10:14209.

  • [9] LaBarre, J., Peterson, K.E., Kachman, M.T., Perng, W., Tang, L., Hao, W., Zhou, L., Karnovsky, A., Gantoral, A., Tellez-Rojo, M.M., Song, P.X.K., and Burant, C.F. (2020). Mitochondrial nutrient utilization underlying the association between metabolites and insulin resistance in adolescents. J. Clin. Endocrinal. Metab., 105(7), 2442-2455.

  • [10] Zhou, L., Lin, H., Chen, K., and Liang, H. (2019). Efficient estimation and computation of parameters and nonparametric functions in generalized semi/non-parametric regression models. Journal of Econometrics, 213, 593--607.

  • [11] Zhou, L. Lin, H. and Song, P. X. K. (2019). Evaluation of functional covariate-environment interaction in the Cox model. The Canadian Journal of Statistics, 47, 204--221.

  • [12] Tang, L., Zhou, L., and Song P.X.K. (2019). Fusion learning algorithm to combine partially heterogeneous Cox models. Computational Statistics, 34, 395--414.

  • [13] Lin, H., Yang, B., Zhou, L., YIP., P., Chen, Y., and Liang, H. (2019). Global kernel estimator and test of varying-coefficient autoregressive model. The Canadian Journal of Statistics, 47(3),487--519.

  • [14] Zhou, L., Lin, H., and Liang, H. (2018). Efficient estimation of the nonparametric mean and covariance functions for longitudinal and sparse functional data. Journal of the American Statistical Association, 113, 1550--1564.

  • [15] Jansen, E. C., Zhou, L.*, Song, P. X. K., Sanchez, B. N., Mercado, A., Hu, H., Solano, M. Peterson, K. E., and Tellez-Rojo M. M. (2018). ( * 共同第一作者) Prenatal lead exposure in relation to age at menarche: results from a longitudinal study in Mexico City. Journal of Developmental Origins of Health and Disease, 9, 467--472.

  • [16] Jansen, E. C., Zhou, L., Perng, W., Song, P.X-K., Tellez-Rojo, M. M., Mercado, A., Peterson, K. E., Cantoral, A. (2018). Vegetable and lean proteins-based and processed meat and refined grains pattern-based dietary patterns in early childhood are associated with pubertal timing in a sex-specific manner: A prospective study of children from Mexico City. Nutrition Research, 56, 41--50.

  • [17] Lin, H., Zhou, F., Wang, Q., Zhou, L., and Qin, J. (2018). Robust and efficient estimation for the treatment effect in causal inference and missing data problems. Journal of Econometrics, 205, 363--380.

  • [18] Li, Y., Wang, S., Song, P.X.K., Wang, N., Zhou, L., and Zhu, J. (2018). Doubly regularized estimation and selection in linear mixed-effects models for high-dimensional longitudinal data. Statistics and Its Interface, 11, 721--737.

  • [19] Tang, L., Bagherjeiran, A., Chaudhuri, S., and Zhou, L. (2018). Learning large scale ordinal ranking model via divide-and-conquer technique. WWW2018, April 23-27, 2018, Lyon, France.

  • [20] Lin, H., Zhou, L, and Wang, B. (2017). Generalized partial linear models with unknown link and unknown baseline functions for longitudinal data. Statistica Sinica, 27, 1281--1298.

  • [21] Zhou, L., Tang, L., Song, A. T., Cibrik, D.M., and Song, P. X. K. (2017). A LASSO method to identify protein signature predicting post-transplant renal graft survival. Statistics in Biosciences, 9(2), 431--452.

  • [22] Zhou, L., Lin, H. and Lin, Y.C. (2016). Education, intelligence, and well-being: Evidence from a semiparametric latent variable transformation model for multiple outcomes of mixed types. Social Indicators Research, 125(3), 1011--1033.

  • [23] Lin, H., Zhou, L., Li, C. and Li, Y. (2014). Semiparametric transformation models for semicompeting survival data. Biometrics, 70, 599--607.

  • [24] Zhou, L., Lin, H., Song, X. and Li, Y. (2014). Selection of latent variables for multiple mixed-outcome models. Scandinavian Journal of Statistics, 41, 1064--1082.

  • [25] Lin, H., Zhou, L. and Zhou, X. (2014). Semiparametric regression analysis of longitudinal skewed data. Scandinavian Journal of Statistics, 41, 1031--1050.

  • [26] Lin, H., Zhou, L., Elashof, R.M. and Li, Y. (2014). Semiparametric latent variable transformation models for multiple mixed outcomes. Statistica Sinica, 24, 833--854.

  • [27] Lin, H., Zhou, L., Peng, H. and Zhou, X.H. (2011). Selection and combination of biomarkers using ROC method for disease classification and prediction. The Canadian Journal of Statistics, 39, 324--343.




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