上海交通大学数学科学学院

教师 FACULTY

师资队伍

FACULTY

罗珊 Shan Luo

长聘教轨副教授 Tenure-track Associate Professor

办公室 Office：

6 号楼 527

办公接待时间 Office Hour：

Friday 2:00-5:00pm

办公室电话 Office Phone：

54740218

E-mail：

sluomath at sjtu.edu.cn

教育背景 Education：

博士，2012，新加坡国立大学

Ph.D., 2012, National University of Singapore

研究兴趣 Research Interests：

高维数据中的模型选择标准、变量选择方法和统计推断

model selection criteria, variable selection methods and statistical inference in high-dimensional data

教育背景/经历　Education

2001-2005, 北京航空航天大学，获学士学位

2005-2008，北京大学，获硕士学位

2008-2012，新加坡国立大学，获博士学位

工作经历　Work Experience

2014.7-至今：上海交通大学长聘教轨副教授
2018.8-2019.7：密歇根大学生物统计系访问学者

科研

In Preparation:

1.Matrix variate regression, with Z.Xu and Z.Chen.

Manuscripts:

1. Forward selection in linear regression models based on robust estimation, with Z.Chen.

Submitted:

1.FDR control in large scale tests of pairwise independence: a first step.

2.Hypothesis testing for high dimensional regression models, with W. Liu.

3. A Portmanteau Local Feature Discrimination Approach to the Classification with High-dimensional Matrix-variate Data, with Z.Xu and Z.Chen.

Publications:

9:S.Luo and Z. Chen. (2021). Sequential interaction group selection by the principle of correlation search for high-dimensional interaction models. Statistica Sinica, 31(1), 197--221.

8: S.Luo and Z. Chen. (2020). A procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification. Journal of Multivariate Analysis, (179) (2020) 104641.

7: S.Luo and Z. Chen. (2020). Feature selection by canonical correlation search in high-dimensional multi-response models with complex group structures. Journal of the American Statistical Association, (115) (2020) 1227--1235.

6.S.Luo.(2020). Variable selection in high-dimensional sparse multiresponse linear regression models. Statistical Papers. (61):1245-1267.

5.S.Luo, J.Xu and Z. Chen. (2015). Extended Bayesian information criterion in the Cox model with a high-dimensional feature space. Annals of the Institute of Statistical Mathematics, (67) (2015) 287--311.

4.S.Luo and Z. Chen. (2014a). Sequential Lasso cum EBIC for feature selection with ultra-high dimensional feature space. Journal of the American Statistical Association,(109) (2014) 1229--1240 .

3.S.Luo and Z. Chen. (2014b). Edge detection in sparse Gaussian graphical models. Computational Statistics and Data Analysis, (70) (2014) 138--152.

2.S.Luo and Z. Chen. (2013a). Extended BIC for linear regression models with diverging number of relevant features and high or ultra-high feature spaces. Journal of Statistical Planning and Inference (143) (2013) 494--504.

1.S.Luo and Z. Chen. (2013b). Selection consistency of EBIC for GLIM with non-canonical links and diverging number of parameters. Statistics and Its Interface, (6) (2013) 275--284.

教学