Hyemin Yeon

Broadly speaking, my research interest lies in studying complex data structures in Statistics and investigating statistical relationships between such complicated data. In particular, I am interested in functional data analysis, resampling methods (e.g., bootstrap, empirical likelihood), dependent data (e.g., time series, spatially dependent data), robust statistics including statistical depth for non-Euclidean data, and causal inferene for complex data. See my PhD thesis , Google Scholar profile, and Github repositories.

* Corrections for published papers are summarized here.

Publications

Yeon, H., Dai, X. and Lopez-Pintado, S. (2025+) Regularized Halfspace Depth for Functional Data. Journal of Royal Statistical Society, Series B [Link]
Yeon, H., Dai, X. and Nordman, D.J. (2024) Bootstrap inference in functional linear regression models with scalar response under heteroscedasticity. Electronic Journal of Statistics, 18(2), 3590-3627 [Link]
Yeon, H., Dai, X. and Nordman, D.J. (2023) Bootstrap inference in functional linear regression models with scalar response. Bernoulli, 29(4), 2599–2626 [Link]

Technical reports

Yeon, H. (2025) Generalization of theoretical results in "Bootstrap inference in functional linear regression models with scalar response". [Link]
Yeon, H., Dai, X. and Nordman, D.J. (2024) Supplement to "Bootstrap inference in functional linear regression models with scalar response under heteroscedasticity". Iowa State University Digital Repository [Link]

Submitted manuscripts

Yeon, H. (2025+) Inference for function-on-function regression: central limit theorem and residual bootstrap. Under major revision by Statistics and Its Interface
Yeon, H., Dai, X. and Nordman, D.J. (2025+) Wild bootstrap for mean response inference in functional linear regression models. Submitted to Statistica Sinica
Yeon, H., Dai, X. and Lopez-Pintado, S. (2025+) Conditional regularized halfspace depth for sparse functional data and its applications. Submitted to the Journal of Computational and Graphical Statistics

Manuscripts in preparation

Son, D.*, Yang, S., Reich, B., and Yeon, H. (2025+) Causal inference with point processes.
Yeon, H. and Lopez-Pintado, S. (2025+) Statistical inference using functional depth.

Talks

Contributed talk, Bias-corrected bootstrap for heteroscedastic functional linear models, International Conference on Econometrics and Statistics 2025, Tokyo, Japan, August 2025 (Online) (Upcoming)
Invited talk, Regularized halfspace depth: pratical insights and applications, Computational and Methodological Statistics 2024, London, United Kingdom, December 2024 (Online)
Invited talk, A novel halfspace depth for dense and sparse functional data, International Conference on Robust Statistics 2024, Fairfax, Virginia, USA, July 2024
Invited departmental seminar, Bootstrap inference in functional linear regression models, Department of Statistics, North Carolina State University, Raleigh, NC, USA, February 2024 (Online)
Invited talk, A new halfspace depth for functional data, Computational and Methodological Statistics 2023, Berlin, Germany, December 2023 (Online)
Contributed talk, A new halfspace depth for functional data, International Conference on Econometrics and Statistics 2023, Tokyo, Japan, August 2023 (Online)
Invited departmental seminar, Bootstrap inference in functional linear regression models, Department of Statistics, University of Washington, Seattle, WA, USA, February 2023 (Online)
Invited talk, The regularized halfspace depth for functional data, Computational and Methodological Statistics 2022, London, United Kingdom, December 2022 (Online)
Invited departmental seminar, Bootstrap inference in functional linear regression models, Department of Statistics, University of Illinois Urbana-Champaign, Champaign, IL, USA, December 2022
Topic-contributed talk, Bootstrap inference in functional linear regression models, Joint Statistical Meetings 2022, Washington DC, USA, August 2022
Contributed talk, The regularized halfspace depth for functional data, International Conference on Robust Statistics 2022, Waterloo, Ontario, Canada, July 2022 (Online)
Contributed talk, Bootstrap inference in functional linear regression models, International Symposium on Nonparametric Statistics 2022, Paphos, Cyprus, June 2022

Software development

BTSinFLRM - Developer [Github]
An R package for bootstrap inference of mean responses or projections in functional linear regression models with scalar response, which includes functions that implement residual, paired, and wild bootstrap methods.
RHD - Developer [Github]
An R package for computing the regularized halfspace depth for functional data.

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