A0995
Title: Domain selection for functional linear models
Authors: Shu-Chin Lin - National Taiwan University (Taiwan) [presenting]
Jane-Ling Wang - University of California Davis (United States)
Abstract: The purpose is to discuss scalar-on-function linear regression, focusing on the domain selection problem. While the functional predictor X(t) is observed over a compact domain, the scalar response Y may be associated with X(t) only on a specific subdomain. The aim is to propose two methods for estimating this domain of association that highlight the challenge of accurately identifying the domain boundary when the coefficient function transitions smoothly to zero. To address this issue, a reproducing kernel Hilbert space (RKHS) framework is adopted, introducing a domain estimator and establishing its asymptotic properties under mild smoothness conditions.
