Functional convolution models. (August 2014)

• Record Type: Journal Article
• Authors: Asencio, Maria; Hooker, Giles; Gao, H Oliver
• Is Part Of: Statistical modelling. Volume 14:Number 4(2014)
• Page Start: 315
• Page End: 335
• Publication Date: 2014-08
• Subjects: Functional data analysis -- time series -- smoothing -- convolution -- historical linear model; 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: 10.1177/1471082X13508262
• Languages: English
• ISSNs: 1471-082X

Abstract

This article considers the application of functional data analysis methods to modelling particulate matter emission profiles from dynamometer experiments. In particular the functional convolution model is introduced as an extension of the distributed lag model to functional (smooth and continuous) observations. We present a penalized ordinary least squares estimator for the model and a novel bootstrap procedure to provide pointwise confidence regions for the estimated convolution functions. The model is illustrated on the Coordinating Research Council E55/59 study of diesel truck emissions.