Statistics and Computing

, Volume 26, Issue 5, pp 945–950 | Cite as

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.


Factor rotation Functional data  Interpretability  Principal components analysis 


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Biological Statistics and Computational BiologyCornell UniversityIthacaUSA
  2. 2.Research School of Finance, Actuarial Studies and Applied StatisticsAustralian National UniversityCanberraAustralia

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