| Sparse logistic principal components analysis for binary data S Lee, JZ Huang, J Hu The annals of applied statistics 4 (3), 1579, 2010 | 140 | 2010 |
| Transfer of antibiotic resistance plasmids in pure and activated sludge cultures in the presence of environmentally representative micro-contaminant concentrations S Kim, Z Yun, UH Ha, S Lee, H Park, EE Kwon, Y Cho, S Choung, J Oh, ... Science of the total environment 468, 813-820, 2014 | 129 | 2014 |
| ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics HJ Chung, CH Park, MR Han, S Lee, JH Ohn, J Kim, J Kim, JH Kim Nucleic acids research 33 (suppl_2), W621-W626, 2005 | 69 | 2005 |
| Sparse principal component analysis for identifying ancestry‐informative markers in genome‐wide association studies S Lee, MP Epstein, R Duncan, X Lin Genetic epidemiology 36 (4), 293-302, 2012 | 51 | 2012 |
| An RKHS approach to robust functional linear regression H Shin, S Lee Statistica Sinica, 255-272, 2016 | 41 | 2016 |
| Canonical correlation analysis for irregularly and sparsely observed functional data H Shin, S Lee Journal of Multivariate Analysis 134, 1-18, 2015 | 28 | 2015 |
| Perturbation of Numerical Confidential Data via Skew-t Distributions S Lee, MG Genton, RB Arellano-Valle Management Science 56 (2), 318-333, 2010 | 28 | 2010 |
| Principal component regression by principal component selection H Lee, YM Park, S Lee Communications for Statistical Applications and Methods 22 (2), 173-180, 2015 | 23 | 2015 |
| A coordinate descent MM algorithm for fast computation of sparse logistic PCA S Lee, JZ Huang Computational statistics & data analysis 62, 26-38, 2013 | 22 | 2013 |
| M-type smoothing spline estimators for principal functions S Lee, H Shin, N Billor Computational statistics & data analysis 66, 89-100, 2013 | 21 | 2013 |
| A biclustering algorithm for binary matrices based on penalized Bernoulli likelihood S Lee, JZ Huang Statistics and Computing 24, 429-441, 2014 | 17 | 2014 |
| Functional linear regression model with randomly censored data: Predicting conversion time to Alzheimer’s disease SJ Yang, H Shin, SH Lee, S Lee, ... Computational statistics & data analysis 150, 107009, 2020 | 14 | 2020 |
| Label-noise resistant logistic regression for functional data classification with an application to Alzheimer's disease study S Lee, H Shin, SH Lee Biometrics 72 (4), 1325-1335, 2016 | 11 | 2016 |
| Convex clustering for binary data H Choi, S Lee Advances in Data Analysis and Classification 13 (4), 991-1018, 2019 | 10 | 2019 |
| Principal components analysis for binary data S Lee Texas A&M University, 2009 | 10 | 2009 |
| A convenient approach for penalty parameter selection in robust lasso regression J Kim, S Lee Communications for Statistical Applications and Methods 24 (6), 651-662, 2017 | 7 | 2017 |
| Vulnerability of DNA hybridization in soils is due to Mg2+ ion induced DNA aggregation X Wang, H Kweon, S Lee, H Shin, B Chua, MR Liles, M Lee, A Son Soil Biology and Biochemistry 125, 300-308, 2018 | 6 | 2018 |
| A distribution‐free test of constant mean in linear mixed effects models J Lim, X Wang, S Lee, SH Jung Statistics in medicine 27 (19), 3833-3846, 2008 | 5 | 2008 |
| Marginalized lasso in sparse regression S Lee, S Kim Journal of the Korean Statistical Society 48, 396-411, 2019 | 4 | 2019 |
| Two sample test for high-dimensional partially paired data S Lee, J Lim, I Sohn, SH Jung, CK Park Journal of Applied Statistics 42 (9), 1946-1961, 2015 | 4 | 2015 |
