Statistics and Computing

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

Maximal autocorrelation functions in functional data analysis

  • Giles Hooker
  • Steven RobertsEmail author


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 



Giles Hooker was supported in part by NSF Grants DMS-1053252 and DEB-1353039. Steven Roberts was supported in part by ARC Grant DP140100551.


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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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