[Unsupervised Learning]

• 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)

[Semi-Supervised Learning]

• Park, B.-J. and Park, C. (2022). Multicategory Laplacian support vector machines and their variable selection. Submitted to Computational Statistics and Data Analysis.

• 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)

[Supervised Learning]

• 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)

[Graphical Models]

• 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.

• 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)

[Applications in Biology]

• 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.

• Park, C., and Sohn, I. (2008). A Copula Method for Modeling Directional Dependence of Genes. BMC Bioinformatics, 9:225.

[Applications in Transportation]

• 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.

Proceedings

• 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.