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Biometrical Letters

The Journal of Polish Biometric Society

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1896-3811
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Analysis of multivariate repeated measures data using a MANOVA model and principal components

Mirosław Krzysko / Tadeusz Smiałowski / Waldemar Wołynski
  • Faculty of Mathematics and Computer Science, Adam Mickiewicz University, Umultowska 87, 61-614 Poznan, Poland
  • Other articles by this author:
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Published Online: 2014-12-20 | DOI: https://doi.org/10.2478/bile-2014-0008

Abstract

In this paper we consider a set of T repeated measurements on p characteristics on each of n individuals. The n individuals themselves may be divided and randomly assigned to K groups. These data are analyzed using a mixed effect MANOVA model, assuming that the data on an individual have a covariance matrix which is a Kronecker product of two positive definite matrices. Results are illustrated on a data set obtained from experiments with varieties of winter rye.

Keywords: multivariate repeated measures data (doubly multivariate data); Kronecker product covariance structure; maximum likelihood estimates; mixed MANOVA model; principal component analysis

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About the article

Published Online: 2014-12-20

Published in Print: 2014-12-01


Citation Information: Biometrical Letters, Volume 51, Issue 2, Pages 103–114, ISSN (Online) 1896-3811, DOI: https://doi.org/10.2478/bile-2014-0008.

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© by Mirosław Krzysko. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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