A general framework for functional regression modelling

• Published in: Statistical modelling Vol. 17; no. 1-2; pp. 1 - 35
• Main Authors: Greven, Sonja, Scheipl, Fabian
• Format: Journal Article
• Language: English
• Published: New Delhi, India SAGE Publications 01.02.2017
• Subjects: functional additive mixed model; GAMLSS; functional principal components; gradient boosting; Functional data; penalized splines
• ISSN: 1471-082X; 1477-0342
• DOI: 10.1177/1471082X16681317

Abstract Researchers are increasingly interested in regression models for functional data. This article discusses a comprehensive framework for additive (mixed) models for functional responses and/or functional covariates based on the guiding principle of reframing functional regression in terms of corresponding models for scalar data, allowing the adaptation of a large body of existing methods for these novel tasks. The framework encompasses many existing as well as new models. It includes regression for ‘generalized’ functional data, mean regression, quantile regression as well as generalized additive models for location, shape and scale (GAMLSS) for functional data. It admits many flexible linear, smooth or interaction terms of scalar and functional covariates as well as (functional) random effects and allows flexible choices of bases—particularly splines and functional principal components—and corresponding penalties for each term. It covers functional data observed on common (dense) or curve-specific (sparse) grids. Penalized-likelihood-based and gradient-boosting-based inference for these models are implemented in R packages refund and FDboost, respectively. We also discuss identifiability and computational complexity for the functional regression models covered. A running example on a longitudinal multiple sclerosis imaging study serves to illustrate the flexibility and utility of the proposed model class. Reproducible code for this case study is made available online.