Maximal autocorrelation functions in functional data analysis
This paper proposes a new factor rotation for the context of functional principal components analysis. This rotation seeks to re-express 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.
|Collections||ANU Research Publications|
|Source:||Statistics and Computing|
|01_Hooker_Maximal_autocorrelation_2015.pdf||2.11 MB||Adobe PDF||Request a copy|
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