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. 2017 Feb;17(1-2):59-85.
doi: 10.1177/1471082X16681875. Epub 2017 Feb 16.

Comparison and Contrast of Two General Functional Regression Modeling Frameworks

Jeffrey S Morris  1
Affiliations

Comparison and Contrast of Two General Functional Regression Modeling Frameworks

Jeffrey S Morris. Stat Modelling. 2017 Feb.

Abstract

In this article, Greven and Scheipl describe an impressively general framework for performing functional regression that builds upon the generalized additive modeling 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 respecitve strengths and weaknesses, and providing recommendations regarding the settings in which each approach might be preferable.

Keywords: Bayesian modeling; Functional data analysis; Functional regression; Linear Mixed Models.

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References

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