Comparison and contrast of two general functional regression modelling frameworks. (February 2017)

• Record Type: Journal Article
• Authors: Morris, Jeffrey S.
• Is Part Of: Statistical modelling. Volume 17:Number 1/2(2017)
• Page Start: 59
• Page End: 85
• Publication Date: 2017-02
• Subjects: Bayesian modeling -- Functional data analysis -- functional regression -- functional mixed models -- linear mixed models; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X16681875
• Languages: English
• ISSNs: 1471-082X

Abstract

Abstract: In their article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modelling framework. Over the past number of years, my collaborators and I have also been developing a general framework for functional regression, functional mixed models, which shares many similarities with this framework, but has many differences as well. In this discussion, I compare and contrast these two frameworks, to hopefully illuminate characteristics of each, highlighting their respective strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable.