Maximal Autocorrelation Functions in Functional Data Analysis
Methodology
2014-07-18 v1
Abstract
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.
Cite
@article{arxiv.1407.4578,
title = {Maximal Autocorrelation Functions in Functional Data Analysis},
author = {Giles Hooker and Steven Roberts},
journal= {arXiv preprint arXiv:1407.4578},
year = {2014}
}
Comments
10 pages 2 figures