Maximal Autocorrelation Functions in Functional Data Analysis, Giles Hooker, Steven Roberts, 7-17-14

Maximal Autocorrelation Functions in Functional Data Analysis

Giles Hooker, Steven Roberts(Submitted on 17 Jul 2014)

This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-represent a functional subspace in terms of directions of decreasing smoothness as represented by a generalized smoothing metric. The rotation can be implemented simply and we show on two examples that this rotation can improve the interpretability of the leading components.

Comments: 10 pages 2 figuresSubjects: Methodology (stat.ME)Cite as: arXiv:1407.4578 [stat.ME]  (or arXiv:1407.4578v1 [stat.ME] for this version)