Publications



(* indicates a Ph.D. student under supervision)


  • So, J., Ahn, J., and Guan, M. (2022), Beyond depth and breadth: Taking "types" of health information sought into consideration with cluster analysis, Journal of Health Communication, accepted.

  • Chang, W., Ahn, J., and Jung, S. (2021), Double data piling leads to perfect classification, Electronic Journal of Statistics, 15(2), 6382-6428.

  • Kwak, S*., Ahn, J., Lee, J. and Park, C. (2021+), Differentially private goodness of fit tests for continuous variables, Econometrics and Statistics, accepted.

  • Poythress, J. C., Ahn, J., and Park, C. (2021+), Dimension-wise sparse Low-rank approximation of a matrix with application to variable selection in high-dimensional integrative analyses of association, Journal of Applied Statistics, accepted.

  • Poythress, J. C.∗ , Ahn, J., and Park, C. (2021+), A low-rank, orthogonally decomposable tensor regression with application to visual stimulus decoding with fMRI Data, Journal of Computational and Graphical Statistics, accepted.

  • Ma, Z.∗ and Ahn, J. (2021) Feature-weighted ordinal classification for predicting drug response in multiple Myeloma, Bioinformatics, 37(19), 3270-3276.

  • Park, J., Ahn, J., and Jeon, Y. (2021), Sparse functional linear discriminant analysis, Biometrika, asaa107

  • Chung, H. C.∗ and Ahn, J. (2021), Subspace rotations for high-dimensional outlier detection, Journal of Multivariate Analysis, 183, 104713.

  • Poythress, J., Kaiser, E., Scheulin, K., Jurgielewicz, B., Lazar, N., Park, C., Stice, S., Ahn, J., and West, F. (2021), An integrative multivariate approach for predicting functional recovery from MRI parameters in a translational pig ischemic stroke model, Neural Regeneration Research, 16(5), 842--850.

  • Fang, X., Sun, W., Jeon, J., Azain, M., Kinder, H., Ahn, J., Chung, H., Mote, R., Filipov, N., Zhao, Q., Rayalam, S., Park , H.(2020), Perinatal Docosahexaenoic acid supplementation improves cognition and alters brain function organization in piglets, Nutrients, 12(7): 2090

  • Ahn, J., Chung, H. C.∗ , and Jeon, Y. (2020), Trace regularization for high-dimensional multi-class discrimination, Journal of Computational and Graphical Statistics, 30(1), 192–203.

  • Qiu, D.∗ and Ahn, J. (2020), Grouped variable screening for ultrahigh dimensional data under linear model, Computational Statistics and Data Analysis, 144, 106894.

  • Ahn, J., Lee, M. H., and Lee, J.∗ (2019), Distance-based outlier detection for high dimension, low sample size data, Journal of Applied Statistics, 46(1), 13–29.

  • Jung, S., Ahn, J. and Jeon, Y. (2019) Penalized orthogonal iteration for sparse estimation of generalized eigenvalue problem, Journal of Computational and Graphical Statistics, 28(3), 710–721

  • Jung, S., Lee, M. H., and Ahn, J. (2018), On the number of principal components in high dimensions, Biometrika, 105(2), 389–402.

  • Safo, S.∗ , Ahn, J., Jeon, Y. and Jung, S. (2018), Sparse Generalized Eigenvalue Problem for Canonical Correlation Analysis With Application to Integrative Analysis of Methylation and Gene Expression Data, Biometrics, 74(4), 1362–1371.

  • Park, J. and Ahn, J. (2017), Clustering Multivariate Functional Data with Phase Variation, Biometrics, 73(1): 324–333.

  • Kwon, S., Ahn, J., Jang, W., Lee, S., and Kim, Y. (2017), A Doubly Sparse Penalty Approach for Group Variable Selection, Annals of the Institute of Statistical Mathematics, 69:997–1025.

  • Safo, S.∗ and Ahn, J. (2016), General Sparse Multi-class Linear Discriminant Analysis, Computational Statistics and Data Analysis, 99:81–90.

  • Ahn, J. and Jeon, Y. (2015), Sparse HDLSS Discrimination with Constrained Data Piling, Computational Statistics and Data Analysis, 90:74–83.

  • Jeon, Y., Ahn, J., and Park, C. (2015), A Nonparametric Kernel Approach to Interval-Valued Data Analysis, Technometrics, 57 (4), 566-575.

  • Lee, J.∗ , Dobbin, K. K., and Ahn, J. (2014), Covariance Adjustment for Batch Effect in Gene Expression Data, Statistics in Medicine, 33(15):2681–2695.

  • Lee, M. H., Ahn, J. and Jeon, Y. (2013), HDLSS Discrimination with Adaptive Data Piling, Journal of Computational and Graphical Statistics, 22:433-451.

  • Ahn, J., Peng, M., Park, C., Jeon, Y. (2012), A Resampling Approach for Interval-Valued Data Regression, Statistical Analysis and Data Mining, 5:336–348.

  • Ahn, J., Lee, M. H., and Yoon, Y. J. (2012), Clustering High Dimension, Low Sample Size Data Using the Maximal Data Piling Distance, Statistica Sinica, 22(2):443–464.

  • Park, C., Ahn, J., Hendry, M.and Jang, W. (2011), Analysis of long period variable stars with nonparametric tests for trend detection, Journal of the American Statistical Association, 106(495):832–845.

  • Park, E., Spiegelman, C. and Ahn, J. (2011), A Nonparametric Approach Based on a Markov like Property for Classification, Chemometrics and Intelligent Laboratory Systems, 108:87–92.

  • Ahn, J. and Marron, J. S. (2010), The Maximal Data Piling Direction for Discrimination, Biometrika, 97:254–259.

  • Ahn, J. (2010), A Stable Hyperparameter Selection for the Gaussian RBF Kernel for Discrimination, Statistical Analysis and Data Mining, 3:142–148.

  • Park, C., Lazar, N., Ahn, J., and Sornborger, A. (2010), A Multiscale Analysis of the Temporal and Spatial Characteristics of Resting fMRI Data, Journal of Neuroscience Methods, 193:334–342.

  • Liu, Y., Zhang, H. H., Park, C., and Ahn, J. (2007), Support Vector Machines with Adaptive Lq Penalty, Computational Statistics and Data Analysis, 51:6380–6394.

  • Ahn, J., Marron, J. S., Muller, K.E. and Chi, Y. -Y. (2007), The High Dimension, Low Sample Size Geometric Representation Holds Under Mild Conditions, Biometrika, 94:760–766.

  • Marron, J. S., Todd, M. J., and Ahn, J. (2007), Distance Weighted Discrimination, Journal of the American Statistical Association, 102:1267–1271.

  • Zhang, H., Ahn, J., Lin, X., and Park, C. (2006), Gene Selection Using Support Vector Machines with Nonconvex Penalty, Bioinformatics, 22:88–95.

  • Robinson III, W. P., Stiffler, A., Rutherford, E. J., Ahn, J., Hurd, H., Baker, C. C., Meyer, A., and Rich, P. B. (2004), Blood Transfusion is an Independent Predictor of Increased Mortality in Nonoperatively Managed Blunt Hepatic and Splenic Injuries, Journal of Trauma-Injury Infection & Critical Care, 58:437--445.

  • Ahn, J. and Park, S. H. (1999), Optimal Restrictions on Regression Parameters For Linear Mixture Model, Journal of Korean Statistical Society, 28:325--336.