Non-Euclid Data Analysis

• • Li, B.*, Yoon, C.*, and Ahn, J. (2023), Reproducing kernels and new approaches in compositional data analysis, Journal of Machine Learning Research, 24(327):1-34.

  • Park, J.*, Ahn, J., and Park, C. (2023), Kernel sufficient dimension reduction and variable selection for compositional data, Proceedings of the 40th International Conference on Machine Learning (ICML), 202:27034-27047.

  • Lee, S., Jung, S., Lourenco, J., Pringle, D., and Ahn, J. (2023), Resampling-based inferences for compositional regression with application to beef cattle microbiomes, Statistical Methods in Medical Research, 32(1), 151-164.

  • Park, J.*, Yoon, C.*, Park, C., and Ahn, J. (2022), Kernel methods for radial transformed compositional data with many zeros, Proceedings of the 39th International Conference on Machine Learning (ICML), 162: 17458 - 17472. 

  • Ahn, J. and Park, S. H. (1999), Optimal restrictions on regression parameters for linear mixture model, Journal of the Korean Statistical Society, 28:325–336.

High-dimensional Statistical Learning 

• • Kim, M., Han, S., Ahn, J., and Jung, S. (2025), Variable selection and basis learning for ordinal classification, Journal of Computational and Graphical Statistics, accepted.

  • Kim, T., Chang, W., Ahn, J., and Jung, S. (2024), Double Data Piling: A High-Dimensional Solution for Asymptotically Perfect Multi-Category Classification, Journal of the Korean Statistical Society, 1-34.

  • Han, S., Kim, M., Jung, S., and Ahn, J. (2024), Sparse ordinal discriminant analysis, Biometrics, 80(1), ujad040.

  • Yoon, C.*, Jeon, Y., Choi, H., Kwon, S., and Ahn, J. (2023), Interpretable Classification for Multivariate Gait Analysis of Cerebral Palsy, BioMedical Engineering OnLine, 22, 109.

  • Poythress, J. C.*, Ahn, J., and Park, C. (2022), A low-rank, orthogonally decomposable tensor regression with application to visual stimulus decoding with fMRI Data, Journal of Computational and Graphical Statistics, 31(1), 190-203.

  • Poythress, J. C.*, Ahn, J., and Park, C. (2022), 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, 49(15), 3889–3907.

  • Park, J., Ahn, J., and Jeon, Y. (2022), Sparse functional linear discriminant analysis, Biometrika, 109(1), 209-226. 

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

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

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

  • 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.

Data Privacy

• • Kwak, S*., Ahn, J., Lee, J. and Park, C. (2024), Differentially private goodness of fit tests for continuous variables,  Econometrics and Statistics, 31, 81-99.

  • Lee, B.*, Ahn, J., and Park, C. (2024), Differential privacy in scalable general kernel learning via K-means Nystrom random features, Proceedings of the 38th Conference on Neural Information Processing Systems (NeurIPS).

  • Lee, B.*, Ahn, J., and Park, C. (2023),  Minimax risks and optimal procedures for estimation under functional local differential privacy, Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), 2526:57964 - 57975.

  • An, S.*, Doan, T., Lee, J., Kim, J., Kim, YJ., Kim, Y.*, Yoon, C.*, Jung, S., Kim, D., Kwon, S., Kim, HJ., Ahn, J., and Park, C. (2023), A comparison of synthetic data approaches using utility and disclosure risk measures, The Korean Journal of Applied Statistics (in Korean), 36(2), 141-166.

Interdisciplinary Research 

• Jeon, J., Kaiser, E., Waters, E., Yang, X., Lourenco, J., Madison, F., Scheulin, K., Sneed, S., Shin, S., Kinder, H., Kumar, A., Platt, S., Ahn, J., Duberstein, K., Rothrock, M., Callaway, T., Xie, J., West, F., Park, H (2023), Tanshinone IIA-loaded nanoparticles and neural stem cell combination therapy improves gut homeostasis and recovery in a pig ischemic stroke model, Scientific Reports, 13(1):2520

  • 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, 27(1), 27 - 36. 

  • 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 

  • 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.