Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 106 - New Statistical Models for Functional Data/ Longitudinal Data
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #322820
Title: Gaussian Copula Function-on-Scalar Regression in Reproducing Kernel Hilbert Space
Author(s): Haihan Xie and Linglong Kong*
Companies: University of Alberta and University of Alberta
Keywords: Gaussian Copula; Reproducing Kernel Hilbert Space; Function-on-Scalar Regression; optimal convergence rate; conformal prediction; distirbution-free
Abstract:

To relax the linear assumption in function-on-scalar regression, we borrow the strength of copula and propose a novel Gaussian copula function-on-scalar regression. Our model is more flexible to characterize the dynamic relationship between functional response and scalar predictors. Estimation and prediction are fully investigated. We develop a closed form for the estimator of coefficient functions in a reproducing kernel Hilbert space without the knowledge of marginal transformations. Valid, distirbution-free, finite-sample prediction bands are constructed via conformal prediction. Theoretically, we establish the optimal convergence rate on the estimation of coefficient functions and show that our proposed estimator is rate-optimal under fixed and random designs. The finite-sample performance is investigated through simulations and illustrated in real data analysis.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program