In Preparation:
1.Sparse Linear Discriminant Analysis for High dimensional Gaussian matrix-valued predictors, 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 procedure of linear discrimination analysis with detected sparsity structure for high-dimensional multi-class classification, with Z.Chen.
4.Feature selection by canonical correlation search in high-dimensional multi-response models with complex group structures, with Z.Chen.
Publications:
7:S.Luo and Z. Chen. (2019). Sequential interaction group selection by the principle of correlation search for high-dimensional interaction models. Statistica Sinica. (Forthcoming)
6.S.Luo.(2018). Variable selection in high-dimensional sparse multiresponse linear regression models. Statistical Papers. (Forthcoming)
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.
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