3 papers
stat.ME2026
Wild bootstrap for mean response inference in functional linear regression models
Hyemin Yeon, Xiongtao Dai, Daniel Nordman
Functional regressors complicate inference in linear regression problems so that the bootstrap can play a useful role in quantifying uncertainty and calibrating intervals. The best…
stat.ME2026
Inference for function-on-function regression: central limit theorem and residual bootstrap
Hyemin Yeon
We investigate asymptotic inference in a linear regression model where both response and regressors are functions, using an estimator based on functional principal components analy…
stat.ME2025
Projection depth for functional data: Theoretical properties
Filip Bočinec, Stanislav Nagy, Hyemin Yeon
We introduce a novel projection depth for data lying in a general Hilbert space, called the regularized projection depth, with a focus on functional data. By regularizing projectio…