Miller, C., and Bowman, A. (2012) Smooth principal components for investigating changes in covariances over time. Journal of the Royal Statistical Society: Series C (Applied Statistics), 61(5), pp. 693-714. (doi:10.1111/j.1467-9876.2012.01037.x)
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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.
|Glasgow Author(s) Enlighten ID:||Miller, Dr Claire and Bowman, Professor Adrian|
|Authors:||Miller, C., and Bowman, A.|
|College/School:||College of Science and Engineering > School of Mathematics and Statistics > Statistics|
|Journal Name:||Journal of the Royal Statistical Society: Series C (Applied Statistics)|
|Published Online:||24 April 2012|
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