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Maximal Autocorrelation Functions in Functional Data Analysis

2014/07/17 by Giles Hooker, Hooker, Giles, Steven Roberts +1
Agricultural and Biological Sciences · Chemistry · Mathematics · #Advanced Statistical Methods and Models #Sensory Analysis and Statistical Methods #Spectroscopy and Chemometric Analyses #stat.ME

paper · pdf · doi:10.48550/arxiv.1407.4578

10 pages 2 figures

arxiv created 2014/07/17 · arxiv updated 2014/07/18

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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