Penalized function-on-function regression

Andrada Ivanescu (), Ana-Maria Staicu (), Fabian Scheipl () and Sonja Greven ()

Computational Statistics, 2015, vol. 30, issue 2, 539-568

Abstract: A general framework for smooth regression of a functional response on one or multiple functional predictors is proposed. Using the mixed model representation of penalized regression expands the scope of function-on-function regression to many realistic scenarios. In particular, the approach can accommodate a densely or sparsely sampled functional response as well as multiple functional predictors that are observed on the same or different domains than the functional response, on a dense or sparse grid, and with or without noise. It also allows for seamless integration of continuous or categorical covariates and provides approximate confidence intervals as a by-product of the mixed model inference. The proposed methods are accompanied by easy to use and robust software implemented in the pffr function of the R package refund. Methodological developments are general, but were inspired by and applied to a diffusion tensor imaging brain tractography dataset. Copyright Springer-Verlag Berlin Heidelberg 2015

Keywords: Functional data analysis; Functional regression model; Mixed model; Multiple functional predictors; Penalized splines; Tractography data (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:30:y:2015:i:2:p:539-568

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DOI: 10.1007/s00180-014-0548-4

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