Joint modelling of paired sparse functional data using principal components. Academic Article

abstract

• We propose a modelling framework to study the relationship between two paired longitudinally observed variables. The data for each variable are viewed as smooth curves measured at discrete time-points plus random errors. While the curves for each variable are summarized using a few important principal components, the association of the two longitudinal variables is modelled through the association of the principal component scores. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed-effects model framework for model fitting, prediction and inference. The proposed method can be applied in the difficult case in which the measurement times are irregular and sparse and may differ widely across individuals. Use of functional principal components enhances model interpretation and improves statistical and numerical stability of the parameter estimates.

authors

• Carroll, Raymond
• Zhou, Lan

published proceedings

• Biometrika

author list (cited authors)

• Zhou, L., Huang, J. Z., & Carroll, R. J.

citation count

• 105

complete list of authors

• Zhou, Lan||Huang, Jianhua Z||Carroll, Raymond J

publication date

• September 2008

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biometrika  Journal

keywords

• Functional Data
• Longitudinal Data
• Mixed-effects Model
• Penalized Spline
• Principal Component
• Reduced-rank Model

PubMed Central ID

• 19396364

Digital Object Identifier (DOI)

• 10.1093/biomet/asn035

start page

• 601

end page

• 619

volume

• 95

issue

• 3

URL

• http://dx.doi.org/10.1093/biomet/asn035