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Canonical Correlation Analysis In The Case Of Multivariate Repeated Measures Data

Author

Listed:
  • Krzysko Mirosław

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

  • Łukaszonek Wojciech

    (Interfaculty Institute of Mathematics and Statistics, The President Stanistaw Wojciechowski State University of Applied Sciences, Kalisz, Poland)

  • Wołynski Waldemar

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

Abstract

In this paper, we present, in the real example, canonical variables applicable in the case of multivariate repeated measures data under the following assumptions: (1) multivariate normality for the vector of observations and (2) Kronecker product structure of the positive definite covariance matrix. These variables are especially useful when the number of observations is not large enough to estimate the covariance matrix, and thus the traditional canonical variables fail. Computational schemes for maximum likelihood estimates of required parameters are also given.

Suggested Citation

  • Krzysko Mirosław & Łukaszonek Wojciech & Wołynski Waldemar, 2018. "Canonical Correlation Analysis In The Case Of Multivariate Repeated Measures Data," Statistics in Transition New Series, Statistics Poland, vol. 19(1), pages 75-85, March.
    • Handle: RePEc:vrs:stintr:v:19:y:2018:i:1:p:75-85:n:7
    DOI: 10.21307/stattrans-2018-005
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    References listed on IDEAS

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    1. Mirosław Krzyśko & Michał Skorzybut, 2009. "Discriminant analysis of multivariate repeated measures data with a Kronecker product structured covariance matrices," Statistical Papers, Springer, vol. 50(4), pages 817-835, August.
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