Smooth principal components for investigating changes in covariances over time
Authors: Miller, Claire; Bowman, Adrian
Source: Journal of the Royal Statistical Society: Series C (Applied Statistics), Volume 61, Number 5, 1 November 2012 , pp. 693-714(22)
Abstract:Summary. The complex interrelated nature of multivariate systems can result in relationships and covariance structures that change over time. Smooth principal components analysis is proposed as a means of investigating whether and how the covariance structure of multiple response variables changes over time, after removing a smooth function for the mean, and this is motivated and illustrated by using data from an aircraft technology study and a lake ecosystem. Inferential procedures are investigated in the cases of independent and dependent errors, with a bootstrapping procedure proposed to detect changes in the direction or variance of components.
Document Type: Research Article
Affiliations: University of Glasgow, UK
Publication date: 2012-11-01