Peer-reviewed Journals
-
(*: corresponding author, ^: graduate student under supervision)
-
-
● Shin, W.^ and Jung, Y.* (2023) Deep Support vector quantile regression with non-crossing constraints. Computational Statistics, 38, 1947 – 1976.
-
● Lee, D.^ and Jung, Y.* (2022) Tutorial and applications of convolutional neural network models in image classification. Journal of the Korean Data & Information Science Society, 33 (3), 533 – 549.
-
● Jeong, J.^ and Jung, Y.* (2022) Wafer Bin Map Failure Pattern Recognition Using Hierarchical Clustering. The Korean Journal of Applied Statistics, 35 (3), 407 – 419. (Written in Korean)
-
● Park, J.^ and Jung, Y.* (2022) A Review and Comparison of Convolution Neural Network Models under a Unified Framework. Communications for Statistical Applications and Methods, 29 (2), 161 – 176.
-
● Son, M. , Choi, T., Shin, S. J., Jung, Y., and Choi, S. (2022) Regularized Linear Censored Quantile Regression. Journal of the Korean Statistical Society. 51, 589 – 607.
-
● Jung, Y.* and Kim, H.^ (2022) Weighted Validation of Heteroscedastic Regression Models for Better Selection, Statistical Analysis and Data Mining: The ASA Data Science Journal. 15, 57 – 68.
-
● Shin, W.^, Kim, M.^, and Jung, Y.* (2022) Efficient Information-based Criteria for Model Selection in Quantile Regression. Journal of the Korean Statistical Society. 51, 245 – 281.
-
● Shin, W.^ and Jung, Y.* (2021) Efficient information-based quantile regression model tuning with heteroscedastic errors. Journal of the Korean Data & Information Science Society, 32 (5), 917 – 929. (Written in Korean)
-
● Min, S.^ and Jung, Y.* (2021) Comparative Study of Prediction Models for Public Bicycle Demand in Seoul. Journal of the Korean Data & Information Science Society, 32 (3), 585 – 592. (Written in Korean)
-
● Lee, H. J.^ and Jung, Y.* (2021) Comparison of Deep Learning-based Autoencoders for Recommender Systems. The Korean Journal of Applied Statistics, 34 (3), 329 – 345. (Written in Korean)
-
● Han, H.^ and Jung, Y.* (2021) Comparison of Audio Input Representations on Piano Transcription Using Neural Networks. Journal of the Korean Data & Information Science Society, 32 (2), 439 – 453.
-
● Jung, Y.*, MacEachern, S. N., and Kim, H. (2021) Modified Check Loss for Efficient Estimation via Model Selection in Quantile Regression, Journal of Applied Statistics, 48 (5), 866 – 886.
-
● Jung, Y.* (2020) Optimal Regression Parameter-specific Shrinkage by Plug-in Estimation, Communications in Statistics – Theory and Methods, 49 (18), 4490 – 4505.
-
● Kim, D.^ and Jung, Y.* (2019) A Numerical Study on Group Quantile Regression Models. Communications for Statistical Applications and Methods, 26 (4), 359 – 370.
● Jung, Y.* (2019) Nonlinear Regression Models for Heterogeneous Data with Massive Outliers, Journal of Applied Statistics, 46 (8), 1456 – 1477.
● Jung, Y.* and Hu, J. (2019) Review: Reversed Low-rank ANOVA Model for Transforming High Dimensional Genetic Data into Low Dimension, Journal of the Korean Statistical Society, 48 (2), 169 – 314.
● Jung, Y., Zhang, H., and Hu, J. (2019) Transformed Low-rank ANOVA Models for High-dimensional Variable Selection, Statistical Methods in Medical Research, 28 (4), 1230 – 1246.
-
● De Mello Costa, M.F., Ronchi, F.A., Jung, Y., Ivanow, A., Brage, J.V., Ramos. M.T., Casarini, D.E., and Slocombe, R.F. (2018) ACE Activity Post-race is Influenced by Furosemide Administration, Comparative Exercise Physiology , 14 (2), 119 – 125.
● Jung, Y.* (2018) Multiple Predicting K-fold Cross-validation for Model Selection, Journal of Nonparametric Statistics , 30 (1), 197 – 215.
● Jung, Y.* (2017) Shrinkage Estimation of Proportion via Logit Penalty, Communications in Statistics - Theory and Methods, 46 (5), 2447 – 2453.
● Hardie, C., Jung, Y., and Jameson, M. (2016) Effect of Statin and Aspirin Use on Toxicity and Pathological Complete Response Rate of Neo-adjuvant Chemoradiation for Rectal Cancer. Asia-Pacific Journal of Clinical Oncology, 12, 167 – 173.
● Jung, Y.*, Lee, S. P., and Hu, J. (2016) Robust Regression for Highly Corrupted Response by Shifting Outliers. Statistical Modelling, 16 (1), 1 – 23.
● Jung, Y.*, and Hu, J. (2015) A K-fold Averaging Cross-validation Procedure. Journal of Nonparametric Statistics, 27 (2), 167 – 179. [Journal of Nonparametric Statistics Best Paper Award 2015]
● Jung, Y., Lee, Y., and MacEachern, S. N. (2015) Efficient Quantile Regression for Heteroscedastic Models. Journal of Statistical Computation and Simulation, 85 (13), 2548 – 2568.
● Jung, Y., Hu, J., and Huang, J. (2014) Biomarker Detection in Association Studies: Modeling SNPs Simultaneously via Logistic ANOVA. Journal of the American Statistical Association, 109 (508), 1355 – 1367.
● Yoo, J., Kim, J., Ro, S., Jung, Y., Jung, S., Choo, S., Lee, J., and Chung, C. (2014) Impact of concomitant surgical atrial fibrillation ablation in patients undergoing aortic valve replacement. Circulation Journal, 78 (6), 1364 – 1371.
● Lester, J., Wessels, A., and Jung, Y. (2014) Oncology Nurses' Knowledge of Survivorship Care Planning: The Need for Education. Oncology Nursing Forum, 41 (2), E35 – E43.
● Lee, Y., MacEachern, S. N., and Jung, Y. (2012) Regularization of Case-Specific Parameters for Robustness and Efficiency. Statistical Science, 27 (3), 350 – 372.
● Lee, S., Lee I., Jung, Y. , McConkey, D., and Czerniak, B. (2012) In-Frame cDNA library combined with protein complementation assay identifies ARL11-binding partners. PLoS ONE, 7(12): e52290.
Conference Proceedings
● Jung, Y., and MacEachern, S. N. (2016) Efficient Model Selection in Linear and Non- linear Quantile Regression by Cross-validation. Proceedings of International Conference on Computational and Statistical Sciences 2016, Paris, France.
Technical Reports
● Jung, Y., MacEachern, S. N., and Lee, Y. (2010) Window Width Selection for L2 Adjusted Quantile Regression. Technical Report No. 835, Department of Statistics, The Ohio State University.