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Canonical correlation analysis for irregularly and sparsely observed functional data

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  • Shin, Hyejin
  • Lee, Seokho

Abstract

Several approaches for functional canonical correlation analysis have been developed to measure the association between paired functional data. However, the existing methods in the literature have been developed for dense and balanced functional data, and they cannot be directly applicable to the situations where the observed curves are recorded in the irregular and sparse fashion. In this paper, we model the associations between paired functional data into a linear mixed-effects model framework by relating two sets of curves using canonical correlation analysis. The proposed approach automatically deals with irregularly or sparsely observed functional data, and brings a new insight into the interpretation of canonical correlation analysis. Numerical studies are carried out to demonstrate finite sample behavior. Two real data applications are provided to illustrate the methodology.

Suggested Citation

  • Shin, Hyejin & Lee, Seokho, 2015. "Canonical correlation analysis for irregularly and sparsely observed functional data," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 1-18.
    • Handle: RePEc:eee:jmvana:v:134:y:2015:i:c:p:1-18
    DOI: 10.1016/j.jmva.2014.10.001
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    References listed on IDEAS

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    1. Heungsun Hwang & Kwanghee Jung & Yoshio Takane & Todd Woodward, 2012. "Functional Multiple-Set Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 48-64, January.
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    4. Cupidon, J. & Eubank, R. & Gilliam, D. & Ruymgaart, F., 2008. "Some properties of canonical correlations and variates in infinite dimensions," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1083-1104, July.
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    7. Gareth M. James & Trevor J. Hastie, 2001. "Functional linear discriminant analysis for irregularly sampled curves," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 533-550.
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    Cited by:

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