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Concurrent Functional Linear Regression Via Plug-in Empirical Likelihood

Hsin-Wen Chang*, Ian W. McKeague

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

4 Downloads (CityUHK Scholars)

Abstract

Functional data with non-smooth features (e.g., discontinuities in the functional mean and/or covariance) and monotonicity arise frequently in practice. This paper develops simultaneous inference for concurrent functional linear regression in this setting. We construct a simultaneous confidence band for a functional covariate effect of interest. Along with a Wald-type formulation, our approach is based on a powerful nonparametric likelihood ratio method. Our procedures are flexible enough to allow discontinuities in the coefficient functions and the covariance structure, while accounting for discretization of the observed trajectories under a fixed dense design. A simulation study shows that the proposed likelihood ratio-based procedure outperforms the Wald-type procedure in moderate sample sizes. We apply the proposed methods to studying the effect of age on the occupation time curve derived from wearable device data obtained in an NHANES study. © The Author(s) 2024.
Original languageEnglish
JournalSankhya A
Online published2 Sept 2024
DOIs
Publication statusOnline published - 2 Sept 2024

Bibliographical note

Full text of this publication does not contain sufficient affiliation information. Research Unit(s) information for this record is based on his previous affiliation.

Research Keywords

  • 62J99
  • Accelerometry
  • Bootstrap
  • Functional data analysis
  • Nonparametric likelihood ratio
  • Primary: 62R10
  • Secondary: 62G15

Publisher's Copyright Statement

  • This full text is made available under CC-BY 4.0. https://creativecommons.org/licenses/by/4.0/

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