| Consistent model selection criteria on high dimensions Y Kim, S Kwon, H Choi The Journal of Machine Learning Research 13 (1), 1037-1057, 2012 | 124 | 2012 |
| Global optimality of nonconvex penalized estimators Y Kim, S Kwon Biometrika 99 (2), 315-325, 2012 | 45 | 2012 |
| Gene selection and prediction for cancer classification using support vector machines with a reject option H Choi, D Yeo, S Kwon, Y Kim Computational statistics & data analysis 55 (5), 1897-1908, 2011 | 45 | 2011 |
| Multiclass sparse logistic regression for classification of multiple cancer types using gene expression data Y Kim, S Kwon, SH Song Computational Statistics & Data Analysis 51 (3), 1643-1655, 2006 | 43 | 2006 |
| A new algorithm and theory for penalized regression-based clustering C Wu, S Kwon, X Shen, W Pan Journal of Machine Learning Research 17 (188), 1-25, 2016 | 42 | 2016 |
| Large sample properties of the scad-penalized maximum likelihood estimation on high dimensions S Kwon, Y Kim Statistica Sinica, 629-653, 2012 | 37 | 2012 |
| A modified local quadratic approximation algorithm for penalized optimization problems S Lee, S Kwon, Y Kim Computational Statistics & Data Analysis 94, 275-286, 2016 | 32 | 2016 |
| Moderately clipped lasso S Kwon, S Lee, Y Kim Computational Statistics & Data Analysis 92, 53-67, 2015 | 21 | 2015 |
| Tuning parameter selection for the adaptive Lasso in the autoregressive model S Kwon, S Lee, O Na Journal of the Korean Statistical Society 46 (2), 285-297, 2017 | 20 | 2017 |
| The use of random-effect models for high-dimensional variable selection problems S Kwon, S Oh, Y Lee Computational Statistics & Data Analysis 103, 401-412, 2016 | 20 | 2016 |
| A small review and further studies on the LASSO S Kwon, S Han, S Lee Journal of the Korean Data and Information Science Society 24 (5), 1077-1088, 2013 | 20 | 2013 |
| Network analysis for count data with excess zeros H Choi, J Gim, S Won, YJ Kim, S Kwon, C Park BMC genetics 18, 1-10, 2017 | 18 | 2017 |
| Improving disease prediction by incorporating family disease history in risk prediction models with large-scale genetic data J Gim, W Kim, SH Kwak, H Choi, C Park, KS Park, S Kwon, T Park, S Won Genetics 207 (3), 1147-1155, 2017 | 17 | 2017 |
| Sparse bridge estimation with a diverging number of parameters H Choi, Y Kim, S Kwon Statistics and Its Interface 6 (2), 231-242, 2013 | 13 | 2013 |
| Homogeneity detection for the high-dimensional generalized linear model JJ Jeon, S Kwon, H Choi Computational statistics & data analysis 114, 61-74, 2017 | 11 | 2017 |
| Evaluation of penalized and nonpenalized methods for disease prediction with large-scale genetic data S Won, H Choi, S Park, J Lee, C Park, S Kwon BioMed Research International 2015, 2015 | 9 | 2015 |
| A unified algorithm for the non-convex penalized estimation: The ncpen package D Kim, S Lee, S Kwon arXiv preprint arXiv:1811.05061, 2018 | 7 | 2018 |
| A doubly sparse approach for group variable selection S Kwon, J Ahn, W Jang, S Lee, Y Kim Annals of the Institute of Statistical Mathematics 69, 997-1025, 2017 | 6 | 2017 |
| A doubly sparse approach for group variable selection S Kwon, J Ahn, W Jang, S Lee, Y Kim Annals of the Institute of Statistical Mathematics 69, 997-1025, 2017 | 6 | 2017 |
| A doubly sparse approach for group variable selection S Kwon, J Ahn, W Jang, S Lee, Y Kim Annals of the Institute of Statistical Mathematics 69, 997-1025, 2017 | 6 | 2017 |
