Maximal Autocorrelation Functions in Functional Data Analysis
arXiv:1407.4578
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.
10 pages 2 figures