Penalized functional regression using R package PFLR

Rob Cameron, Tianyu Guan, Haolun Shi and Zhenhua Lin

Journal of Applied Statistics, 2025, vol. 52, issue 11, 2191-2205

Abstract: Penalized functional regression is a useful tool to estimate models for applications where the effect/coefficient function is assumed to be truncated. The truncated coefficient function occurs when the functional predictor does not influence the response after a certain cutoff point on the time domain. The R package PFLR offers an extensive suite of methods for advanced functional regression techniques with penalization. The package implements four distinct methods, each tailored to different models, effectively addressing a range of scenarios. This is demonstrated through simulations as well as an application to particulate matter emissions data. Generic S3 methods are also implemented for each model to help with summary, visualization and interpretation.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:52:y:2025:i:11:p:2191-2205

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DOI: 10.1080/02664763.2025.2457011

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