(* indicates first author or corresponding author)

Working papers

Cho, Y., Li, L. and Johnson, B. (2026+). Semiparametric Estimation of the Mean Outcome Amidst Informative Treatment Discontinuation with Observational Data.

Cho, Y. and Auer, P. (2026+). Mendelian Randomization Analysis under the Accelerated Failure Time Model.

Yang, X., Cho. Y., Wahed, A. and Cheng, Y. (2026+). Imputation-based Q-learning with regression trees for censored survival data.

Cho, Y., Park, J. and Cui, Y. (2026+). Estimation of heterogeneous effect using ensembles for competing risks data.

Yoon, S., Cho, Y. and Lee, M. (2026+). Variable selection by using boosting for generalized linear model.

Cho, Y. and Ghosh, D. (2026+). Bridging linear and nonlinear sufficient dimension reduction procedures.

Statistical Methodology

Causal inference

Cho, Y*., Zhu, Y. and Lee, H. (2026+). Subgroup analysis in observational studies, Communications for Statistical Applications and Methods, revision submitted.

Cho, Y*, Zhu, Y., Kwon, S., Hyun, J. and Hall, C. (2026+). Mediation analysis with multiple mediators for survival data, International Journal of Biostatistics, under revision.

Cho, Y*., Zheng, C., Qi, L., Prentice, R. and Zhang, M-J. (2025). Causal Effect Estimation for Competing Risk Data in Randomized Trial: Adjusting High-dimensional Covariates to Gain Efficiency, Journal of Applied Statistics, 1-19. https://doi.org/10.1080/02664763.2025.2455626.

Cho, Y*. (2024). Regression discontinuity for survival data. Communications for Statistical Applications and Methods, 31(1), 155-178.

Cao, Z., Cho, Y*. and Li, F. (2024), Transporting randomized trial results to estimate counterfactual survival functions in target populations. Pharmaceutical Statistics, https://doi.org/10.1002/pst.2354.

Su, X., Cho, Y*., Ni, L., Liu, L., and Dusseldorp, E. (2023), Refined Moderation Analysis with Categorical Outcomes in Precision Medicine. Statistics in Medicine, 42(4), 470-486 https://doi.org/10.1002/sim.9627.

Cho, Y.*, Hu, C. and Ghosh, D. (2021). Analysis of regression discontinuity designs using censored data. Journal of Statistical Research, 55(1), 225-248.

Cho, Y.*, Rau, A., Reiner, A., Auer, P. (2021). Mendelian randomization analysis with survival outcomes. Genetic Epidemiology, 45(1), 16-23. https://doi.org/10.1002/gepi.22354.

Ghosh, D. and Cho, Y. (2018). Predictive directions for individualized treatment selection in clinical trials. preprint arXiv:1807.03375v1 [stat.ME].

Machine learning and Deep learning

Wang, H., Zeng, L., Sun, T., Cho, Y*. and Ding, Y. (2026+). ICODEN: Ordinary Differential Equation-based Neural Networks for Interval-Censored Data. Lifetime Data Analysis, submitted.

Cho, Y.*, Auer, P. and Ghosh, D. (2024). Nonlinear estimation methods for Mendelian randomization in genetic studies. Sankya B, https://doi.org/10.1007/s13571-023-00309-5.

Cho, Y*., Zhan, X and Ghosh, D. (2022). Nonlinear predictive directions in clinical trials. Computational Statistics & Data Analysis, 174 (107476), https://doi.org/10.1016/j.csda.2022.107476.

Cho, Y*., Molinaro, A., Hu, C. and Strawderman, R. (2022). Regression trees and ensembles for cumulative incidence functions. International Journal of Biostatistics, 18(2) 397-419. doi: 10.1515/ijb-2021-0014.

Cho, Y*. and Ghosh, D. (2021). Quantile-based subgroup identification for randomized clinical trials. Statistics in Biosciences, 13(1), 90-128. https://doi.org/10.1007/s12561-020-09286-z

Cho, Y*. and Ghosh, D. (2020). Bridging linearity-based and kernel-based sufficient dimension reduction. preprint arXiv:2010.15009.

Cho, Y*., Molinaro, A., Hu, C. and Strawderman, R. (2020). Regression Trees for Cumulative Incidence Functions. preprint arXiv:2011.06706 [stat.ME]

Survival analysis

Cho, Y*., Kim, S. and Ahn, K. W. (2025). Efficient estimation for the multivariate Cox model with missing covariates, Statistica Neerlandica, 1-19. https://doi.org/10.1111/stan.70000.

Cho, Y*. and Ghosh, D. (2021). Covariate adjustment via propensity scores for recurrent events in the presence of dependent censoring. Communications in Statistics - Theory and Methods, 50(1), 216-236, DOI: 10.1080/03610926.2019.1634208.

Cho, Y.*, Hu, C. and Ghosh, D. (2018). Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model. Statistics in Medicine, 37(3), 390-404. DOI: 10.1002/sim.7513. https://onlinelibrary.wiley.com/doi/abs/10.1002/sim.7513

Cho, Y*. and Ghosh, D. (2017). A general approach to goodness of fit for U-processes. Statistica Sinica, 27(3), 1175-1192. DOI:10.5705/ss.202014.0141. http://www3.stat.sinica.edu.tw/sstest/oldpdf/A27n311.pdf

Cho, Y*. and Ghosh, D. (2015). Weighted estimation of the accelerated failure time model in the presence of dependent censoring. PLOS ONE, DOI: 10.1371/journal.pone.0124381. http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0124381&type=printable

Collaboration

Gwon, S. H., Cho, Y., Laestadius, L. I., Wang, Y. and Lee, H. J. (2021). Factors Associated with Opposition to a Vape-Free Campus Policy. Metropolitan Universities, 32(1), 124-145.

Ohn, J., Hur, K., Cho, Y., Park, J., Kim, J. Y., Lee, S. J., Park, H. and Mun, J. H. (2021). Developing a predictive model for distinguishing invasive nail unit melanoma from nail unit melanoma in situ. Journal of the European Academy of Dermatology and Venereology, 35(4), 906-911.

Lee, Y., Cho, Y*. and Oh, H. (2021). Multiple chronic conditions and risk of dementia and cognitive impairment among older Americans: findings from the ADAMS. Aging, Neuropsychology and Cognition, 28(4), 493-507. doi.org/10.1080/13825585.2020.1790492.

Jo, G., Cho, S. I., Cho, Y., Ohn, J. and Mun, J-H. (2020). Tumor growth rate as a prognostic factor of acral melanoma in a Korean population. Medicine, 99(21)(e19936). http://dx.doi.org/10.1097/MD.0000000000019936.

Ohn, J., Jo, G., Cho, Y., Sheu, S. L., Cho, K. H. and Mun, J-H. (2018). Assessment of a predictive scoring model for dermoscopy of subungual melanoma in situ. JAMA Dermatology. 154(8), 890-896. doi:10.1001/jamadermatol.2018.1372 https://jamanetwork.com/journals/jamadermatology/article-abstract/2684586.

Ko, K., Gwak, H., Lee, E., Kim, G., Moon, D., Cho, Y. and Kim, S. (2021). Action Recognition Model to Monitor Illegal Dumping using Zoom-In Image Sequence Data. Quantitative Bio-Science, 40(2), 95-102.

Lim, S.S., Hui, L, Ohn, J., Cho, Y., Oh, C. C. and Mun, J-H. (2022). Diagnostic accuracy of dermoscopy for onychomycosis: a systematic review. Frontiers in Medicine. https://doi.org/10.3389/fmed.2022.1048913

Thalji, S., Cortina, C. Frebault, J., Cho, Y., Thorgerson, A., Bergom, C., Huang, C-C and Kong, A. (2023). Associations between Hospital Volume on Overall Survival after Surgery in Older Patients with Breast Cancer. Annals of Surgical Oncology, 30, 6462–6470. https://doi.org/10.1245/s10434-023-13725-8.

Gwon, S. H., Cho, Y., Kim, Y., Paek, S. and Lee, H. J. (2024). Differences in attentional bias toward e-cigarette cues between e-cigarette users and nonusers, Journal of Addictions Nursing, 35(3), 156-165.

Park, J. H., Choi, Y., Jung, Y. J., Lee, T., Kim, H., Cho, Y. and Lee, J. H. (2024). Skin hydration measurement: Comparison between devices and clinical evaluations. Annals of Dermatology, 36(5), 275-281.

Cho, Y*., Lim, J., Yoon, Y., Kim, C., Lee, Y., Choe, Y. and Yu, D. (2025). New Onset of Hair Loss Disorders During the Coronavirus Disease 2019 Pandemic: A Nationwide Populationbased Study. Annals of Dermatology. https://doi.org/10.5021/ad.25.007.