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arXiv · 1407.4578

Maximal Autocorrelation Functions in Functional Data Analysis

Giles Hooker·Steven Roberts
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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.

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Giles Hooker, Steven Roberts. 2014-07-17. Maximal Autocorrelation Functions in Functional Data Analysis. https://arxiv.org/abs/1407.4578

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