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An Extension of the Classical Distance Correlation Coefficient for Multivariate Functional Data with Applications

Author

Listed:
  • Górecki Tomasz

    (Faculty of Mathematics and Computer Science, Colorado State University, Colorado, ; United States)

  • Krzyśko Mirosław

    (Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznan, ; Poland)

  • Ratajczak Waldemar

    (Faculty of Geographical and Geological Sciences, Adam Mickiewicz University, Poznan, ; Poland)

  • Wołyński Waldemar

    (Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Poznan, ; Poland)

Abstract

The relationship between two sets of real variables defined for the same individuals can be evaluated by a few different correlation coefficients. For the functional data we have one important tool: 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 the distance correlation coefficient for a multivariate functional case.

Suggested Citation

  • Górecki Tomasz & Krzyśko Mirosław & Ratajczak Waldemar & Wołyński Waldemar, 2016. "An Extension of the Classical Distance Correlation Coefficient for Multivariate Functional Data with Applications," Statistics in Transition New Series, Statistics Poland, vol. 17(3), pages 449-466, September.
  • Handle: RePEc:vrs:stintr:v:17:y:2016:i:3:p:449-466:n:14
    DOI: 10.21307/stattrans-2016-032
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    References listed on IDEAS

    as
    1. Ferraty, Frédéric & Vieu, Philippe, 2009. "Additive prediction and boosting for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1400-1413, February.
    2. Jacques, Julien & Preda, Cristian, 2014. "Model-based clustering for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 92-106.
    3. Gareth M. James, 2002. "Generalized linear models with functional predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 411-432, August.
    4. Berrendero, J.R. & Justel, A. & Svarc, M., 2011. "Principal components for multivariate functional data," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2619-2634, September.
    5. Székely, Gábor J. & Rizzo, Maria L., 2013. "The distance correlation t-test of independence in high dimension," Journal of Multivariate Analysis, Elsevier, vol. 117(C), pages 193-213.
    6. Székely, Gábor J. & Rizzo, Maria L., 2012. "On the uniqueness of distance covariance," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2278-2282.
    7. Reiss, Philip T. & Ogden, R. Todd, 2007. "Functional Principal Component Regression and Functional Partial Least Squares," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 984-996, September.
    8. P. Robert & Y. Escoufier, 1976. "A Unifying Tool for Linear Multivariate Statistical Methods: The RV‐Coefficient," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(3), pages 257-265, November.
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