| Multiple predicting K-fold cross-validation for model selection Y Jung Journal of Nonparametric Statistics 30 (1), 197-215, 2018 | 358 | 2018 |
| A K-fold averaging cross-validation procedure Y Jung, J Hu Journal of nonparametric statistics 27 (2), 167-179, 2015 | 349 | 2015 |
| Regularization of case-specific parameters for robustness and efficiency Y Lee, SN MacEachern, Y Jung | 55 | 2012 |
| Oncology nurses' knowledge of survivorship care planning: the need for education AL Wessels Oncology Nursing Forum 41 (2), E35, 2014 | 54 | 2014 |
| Impact of concomitant surgical atrial fibrillation ablation in patients undergoing aortic valve replacement JS Yoo, JB Kim, SK Ro, Y Jung, SH Jung, SJ Choo, JW Lee, CH Chung Circulation Journal 78 (6), 1364-1371, 2014 | 33 | 2014 |
| Efficient quantile regression for heteroscedastic models Y Jung, Y Lee, SN MacEachern Journal of Statistical Computation and Simulation 85 (13), 2548-2568, 2015 | 17 | 2015 |
| Biomarker detection in association studies: modeling SNPs simultaneously via logistic ANOVA Y Jung, JZ Huang, J Hu Journal of the American Statistical Association 109 (508), 1355-1367, 2014 | 10 | 2014 |
| Efficient tuning parameter selection by cross-validated score in high dimensional models Y Jung World Academy of Science, Engineering and technology 10 (1), 19-25, 2016 | 9 | 2016 |
| Regularization of case-specific parameters for robustness and efficiency Y Lee, SN MacEachern, Y Jung Department of Statistics, Columbus, The Ohio State University, USA, 2007 | 8 | 2007 |
| Transformed low-rank ANOVA models for high-dimensional variable selection Y Jung, H Zhang, J Hu Statistical Methods in Medical Research 28 (4), 1230-1246, 2019 | 7 | 2019 |
| Robust regression for highly corrupted response by shifting outliers Y Jung, SP Lee, J Hu Statistical Modelling 16 (1), 1-23, 2016 | 7 | 2016 |
| Comparative study of prediction models for public bicycle demand in Seoul S Min, Y Jung The Korean Data & Information Science Society 32 (3), 585-592, 2021 | 6 | 2021 |
| In-frame cDNA library combined with protein complementation assay identifies ARL11-binding partners S Lee, I Lee, Y Jung, D McConkey, B Czerniak PloS one 7 (12), e52290, 2012 | 6 | 2012 |
| A review and comparison of convolution neural network models under a unified framework J Park, Y Jung Communications for Statistical Applications and Methods 29 (2), 161-176, 2022 | 5 | 2022 |
| Reversed low-rank ANOVA model for transforming high dimensional genetic data into low dimension Y Jung, J Hu Journal of the Korean Statistical Society 48 (2), 169-178, 2019 | 4 | 2019 |
| Bandwidth selection for kernel density estimation with a Markov chain Monte Carlo sample HJ Kim, SN MacEachern, Y Jung arXiv preprint arXiv:1607.08274, 2016 | 4 | 2016 |
| Window width selection for l2 adjusted quantile regression Y Jung, SN MacEachern, Y Lee Technical Report 835, Department of Statistics, The Ohio State University, 2010 | 4 | 2010 |
| Efficient information-based criteria for model selection in quantile regression W Shin, M Kim, Y Jung Journal of the Korean Statistical Society, 1-37, 2021 | 3 | 2021 |
| Modified check loss for efficient estimation via model selection in quantile regression Y Jung, SN MacEachern, H Joon Kim Journal of Applied Statistics 48 (5), 866-886, 2021 | 3 | 2021 |
| Nonlinear regression models for heterogeneous data with massive outliers Y Jung Journal of Applied Statistics 46 (8), 1456-1477, 2019 | 3 | 2019 |
