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Data Science pp 243–258Cite as

Correlation Analysis for Multivariate Functional Data

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Abstract

The relationship between two sets of real variables defined for the same individuals can be evaluated by few different correlation coefficients. For the functional data we have only one important tool: the canonical correlations. It is not immediately straightforward to extend other similar measures to the context of functional data analysis. In this work we show how to use commonly known measures of correlation for two sets of variables: \(\mathop{\mathrm{rV}}\nolimits\) coefficient and distance correlation coefficient for multivariate functional case. Finally, these three different coefficients are compared and their use is demonstrated on two real examples.

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznań, Poland

    Tomasz Górecki, Mirosław Krzyśko & Waldemar Wołyński

Authors
  1. Tomasz Górecki
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  2. Mirosław Krzyśko
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  3. Waldemar Wołyński
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Corresponding author

Correspondence to Tomasz Górecki .

Editor information

Editors and Affiliations

  1. Department of Political Sciences, University of Naples Federico II, Napoli, Italy

    Francesco Palumbo

  2. Department of Statistical Sciences Paolo Fortunati, Alma Mater Studiorum, University of Bologna, Bologna, Italy

    Angela Montanari

  3. Department of Statistical Sciences, Sapienza University of Rome, Rome, Italy

    Maurizio Vichi

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Górecki, T., Krzyśko, M., Wołyński, W. (2017). Correlation Analysis for Multivariate Functional Data. In: Palumbo, F., Montanari, A., Vichi, M. (eds) Data Science . Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-55723-6_19

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