Functional linear mixed models for irregularly or sparsely sampled data. (February 2016)

• Record Type: Journal Article
• Authors: Cederbaum, Jona; Pouplier, Marianne; Hoole, Phil; Greven, Sonja
• Is Part Of: Statistical modelling. Volume 16:Number 1(2016)
• Page Start: 67
• Page End: 88
• Publication Date: 2016-02
• Subjects: functional additive mixed models -- Dependent functional data -- functional principal component analysis -- penalized splines -- speech production; 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: 10.1177/1471082X15617594
• Languages: English
• ISSNs: 1471-082X

Abstract

We propose an estimation approach to analyse correlated functional data, which are observed on unequal grids or even sparsely. The model we use is a functional linear mixed model, a functional analogue of the linear mixed model. Estimation is based on dimension reduction via functional principal component analysis and on mixed model methodology. Our procedure allows the decomposition of the variability in the data as well as the estimation of mean effects of interest, and borrows strength across curves. Confidence bands for mean effects can be constructed conditionally on estimated principal components. We provideR -code implementing our approach in an online appendix. The method is motivated by and applied to data from speech production research.