Longitudinal dynamic functional regression

Document Type

Article

Publication Date

1-1-2020

Abstract

The paper develops a parsimonious modelling framework to study the time-varying association between scalar outcomes and functional predictors observed at many instances, in longitudinal studies. The methods enable us to reconstruct the full trajectory of the response and are applicable to Gaussian and non-Gaussian responses. The idea is to model the time-varying functional predictors by using orthogonal basis functions and to expand the time-varying regression coefficient by using the same basis. Numerical investigation through simulation studies and data analysis show excellent performance in terms of accurate prediction and efficient computations, when compared with existing alternatives. The methods are inspired and applied to an animal science application, where of interest is to study the association between the feed intake of lactating sows and the minute-by-minute temperature throughout the 21 days of their lactation period. R code and an R illustration are provided.

Publication Title

Journal of the Royal Statistical Society. Series C: Applied Statistics

Volume

69

Issue

1

First Page

25

Last Page

46

Digital Object Identifier (DOI)

10.1111/rssc.12376

ISSN

00359254

E-ISSN

14679876

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