An bold-faced name designates main/corresponding author.
Choi, H., Poythress, J. C., Park, C., Jeon, J.-J., and Park, C. (2019). Regularized boxplot via convex clustering. Journal of Statistical Computation and Simulation. 89:7, 1227–1247.
Choi, H. and Park, C. (2016). Shrinkage boxplot for outlier detection. Journal of the Korean Data Analysis Society. 18(5), 2435–2443. (In Korean)
Park, B.-J. and Park, C. (2023). Multiclass Laplacian support vector machine with functional analysis of variance decomposition. Computational Statistics and Data Analysis. 187, 107814.
Choi, B.-J., Kim, K.-R., Cho, K.-D., Park, C., Koo, J.-Y. (2014). Variable Selection for Naive Bayes Semi-supervised Learning. Communications in Statistics - Simulation and Computation. 43(10), 2702–2713.
Lee, S. J., Park, C., and Koo, J.-Y. (2011). Feature selection in the Laplacian Support Vector Machine. Computational Statistics and Data Analysis. 55(1), 567-577.
Choi, B.-J., Chae, Y.-S., Choi, W.-Y., Park, C., and Koo, J.-Y. (2008). Mixture Discriminant Analysis for Semi-Supervised Learning. The Korean Journal of Applied Statistics, 21(5), 825–833. (In Korean)
Hwang, H., Choi, H. and Park, C.(2022). Variable selection for nonlinear support vector machines via elastic net penalty. Journal of the Korean Data & Information Science Society. (In Korean)
Kang, I., Park, C., Yoon, Y. J., Park, C., Kwon, S.-S., and Choi, H. (2021). Classification of histogram-valued data with support histogram machines. Journal of Applied Statistics. In Press.
Bak, K.-Y., Kim, K.-R., Kim, P. T., Koo, J.-Y., Park, C., and Zhu, H. (2021). Nonparametric matrix regression function estimation over symmetric positive definite matrices. Journal of the Korean Statistical Society. 50(3), 795-817.
Shin, J., and Park, C.(2021). FDR-based categorical variable selection in naive Bayes classification. Journal of the Korean Data & Information Science Society. 32(6), 1329-1341. (In Korean)
Park, B.-J. and Park, C.(2021). Kernel variable selection for multicategory support vector machines. Journal of Multivariate Analysis. 186, Article 104800.
Park, K., and Park, C.(2021). Comparison of nonlinear classification methods for image data. Journal of the Korean Data & Information Science Society. 32(4), 767-780. (In Korean)
Jang, Y., Park, B.-J., and Park, C.(2019). Comparison study of K-nearest neighborhood classification algorithms. Journal of the Korean Data & Information Science Society. 30(5), 977–985. (In Korean)
Park, B.-J. and Park, C.(2018). Comparison study of classification methods for image data. Journal of the Korean Data & Information Science Society. 29, 267–276. (In Korean)
Choi, H., Kim, Y., Kwon, S., and Park, C. (2017). A robust support vector machine with labeling errors. Communications in Statistics - Simulation and Computation. 46(8),6061–6073.
Choi, D., Choi, H., and Park, C.(2016). Classification of ratings in online reviews. Journal of Korean Data & Information Science Society. 27(4), 845–854. (In Korean)
Kim, M.S., Choi, H., and Park, C.(2015). Categorical variable selection in Naive Bayes Classification. The Korean Journal of Applied Statistics. 28(3), 407–415. (In Korean)
Choi, H., Koo, J.-Y., and Park, C.(2015). Fused lasso for credit scoring. Journal of Statistical Computation and Simulation. 85(11), 2135–2147.
Choi, H., Park, H., and Park, C.(2013). Support vector machines for big data analysis. Journal of Korean Data & Information Science Society. 24(5), 989–998. (In Korean)
Kim, K. and Park, C.(2013). Comparison of feature selection methods in support vector machines. The Korean Journal of Applied Statistics. 26(1), 131–139. (In Korean)
Koo, J.-Y., Park, K. W., Kim, B. W., Kim, K. R. and Park, C.(2013). Structured kernel quantile regression. Journal of Statistical Computation and Simulation. 83(1), 179–190.
Park, C., Kim, K. R., Myung, R., and Koo, J.-Y. (2012). Oracle properties of SCAD-penalized support vector machine. Journal of Statistical Planning and Inference. 142(8), 2257-2270.
Choi, H. and Park, C.(2012). Approximate penalization path for smoothly clipped absolute deviation. Journal of Statistical Computation and Simulation. 82(5), 643–652.
Jin, S. K., Kim, K. R., and Park, C.(2012). Cutpoint selection via penalization in in credit scoring. The Korean Journal of Applied Statistics, 25(2), 261-267. (In Korean)
Yeo, J.-G., Park, C., and Koo, J.-Y. (2011). A comparison study of cutpoint selection methods in credit scoring . Journal of the Korean Data Analysis Society, 13(6), 2915–2923. (In Korean)
Park, C.(2009). Convergence Rates of Generalization Errors for Margin-based Classification. Journal of Statistical Planning and Inference. 139(8), 2543–2551.
Koo, J., Park, C., Jhun, M. (2009). A Classification Spline Machine for Building a Credit Scorecard. Journal of Statistical Computation and Simulation, 79(5), 681–689.
Ha, J. H. and Park, C.(2009). Variable Selection in Linear Discriminant Analysis. Journal of the Korean Data Analysis Society, 11(1), 381–389. (In Korean)
Koo, J.-Y., Lee, Y., Kim, Y. and Park, C.(2008). A Bahadur Representation for the Linear Support Vector Machine. Journal of Machine Learning Research, 9, 1343–1368.
Park, C., Koo, J. -Y., Kim, P. T., and Lee, J. W. (2008). Stepwise Feature Selection Using the Generalized Logistic Loss. Computational Statistics and Data Analysis, 52(7), 3709–3718.
Park, C., Koo, J. -Y., Kim, S., Sohn, I., and Lee, J. (2008). Classification of Gene Functions Using Support Vector Machine for Time-Course Gene Expression Data. Computational Statistics and Data Analysis, 52(5), 2578–2587.
Park, C.(2007). When Can Support Vector Machines Achieve Fast Rates of Convergence? Journal of Korean Statistical Society, 36(3), 367–372.
Lee, S. J., Park, C., Jhun, M. and Koo, J. -Y. (2007). Support Vector Machine Using K-Means Clustering. Journal of Korean Statistical Society, 36(1), 175–182.
Song, S. H., Kim, K. H., Park, C., and Koo, J.-Y. (2007). Gene Selection Based on Support Vector Machine Using Bootstrap. The Korean Journal of Applied Statistics, 20(3), 531–540. (In Korean)
Park, B.-J., Choi, H., and Park, C.(2021). Negative binomial graphical model with excess zeros. Statistical Analysis and Data Mining. 14, 449-465.
Choi, H., Gim, J., Won, S., Kim, Y. J., Kwon, S., and Park, C.(2017). Network analysis of count data with excess zeros. BMC Genetics, 18:93.
Ahn, H. and Park, C.(2014). Comparison of model selection criteria in graphical LASSO. Journal of the Korean Data & Information Science Society. 25(4), 881–891. (In Korean)
Kim, J., Sohn, I., Jung, S.-H., Kim, S., and Park, C.(2012). Analysis of survival data with group lasso. Communications in Statistics - Simulation and Computation. 41(9), 1593–1605.
Gim, J., Kim, W., Kwak, S. H., Choi, H., Park, C., Park, K.S., Kwon, S., Park, T., and Won, S. (2017). Improving Disease Prediction by Incorporating Family Disease History in Risk Prediction Models with Large-Scale Genetic Data. Genetics. 207(3), 1147–1155.
Won, S., Choi, H., Park, S., Lee, J., Park, C., and Kwon, S. (2015). Evaluation of penalized and non-penalized methods for disease prediction with large-scale genetic data. BioMed Research International. vol. 2015, Article ID 605891, 10 pages. doi:10.1155/2015/605891.
Sohn, I., Kim, J., Jung, S.-H. and Park, C.(2009). Gradient Lasso for Cox Proportional Hazards Model. Bioinformatics, 25(14), 1775–1781.
Kim, J.-M., Jung, Y.-S., Sungur, E. A., Han, K.-H., Park, C., and Sohn, I. (2008). A Copula Method for Modeling Directional Dependence of Genes. BMC Bioinformatics, 9:225.
Goel, P. K., McCord, M. R. and Park, C. (2005). Exploiting Correlations between Link Flows to Improve Estimation of Average Annual Daily Traffic on Coverage Count Segments: Methodology and Numerical Study. *Transportation Research Record: Journal of the Transportation Research Board}, No. 1917, 100–107.
Lee, S., Ahn, S., Park, C., and Jeon, J. (2002). Spatial-Temporal Modeling of Road Traffic Data in Seoul City. Journal of Korean Data & Information Science Society, 13(2), 261–270.
McCord, M. R., Goel, P. K., Jiang, Z., and Park, C. (2006). Improved AADT Estimation on Coverage Count Segments from Volume Correlations with Multiple ATR-Equipped Segments: Empirical Result from Ohio Highways. No. 06-1296, TRB 2006 Annual Meeting.
Ahn, S., Park, C. and Lee, J. (2000). Prediction of missing data using spatial model: Application to ITS. Proceedings of the 7th World Congress on Intelligent Transport Systems, Turin, Italy.
Koo, J.-Y., Kim, K.-R., and Park, C. (2014). Computational statistics in R. Free Academy. ISBN 979-11-5808-049-5. (In Korean).
Park, C., Kim, Y., Kim, J., Song, J., and Choi, H. (2011). Data mining with R. Kyowoo Publisher. ISBN 978-89-8172-893-9. (In Korean).