6 papers · 1 filter
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…
Conditional regularized halfspace depth for sparse functional data and its applications
Hyemin Yeon, Xiongtao Dai, Sara Lopez-Pintado
Many functional datasets are observed sparsely and irregularly. Ordering such data is challenging because only limited information is available from each observation, while the und…
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…
Projection depth for functional data: Practical issues, computation and applications
Filip Bočinec, Stanislav Nagy, Hyemin Yeon
Statistical analysis of functional data is challenging due to their complex patterns, for which functional depth provides an effective means of reflecting their ordering structure.…
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…
Regularized Halfspace Depth for Functional Data
Hyemin Yeon, Xiongtao Dai, Sara Lopez-Pintado
Data depth is a powerful nonparametric tool originally proposed to rank multivariate data from center outward. In this context, one of the most archetypical depth notions is Tukey'…