Biometrical Letters vol. 46(2), 2009, pp. 163-172


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PRINCIPAL COMPONENT ANALYSIS IN THE CASE OF MULTIVARIATE
REPEATED MEASURES DATA

Karol Deręgowski1, Mirosław Krzyśko1,2

1President Stanislaw Wojciechowski Higher Vocational State School in Kalisz, Institute
of Management, Poland, e-mail: kadere@o2.pl
2Adam Mickiewicz University, Faculty of Mathematics and Computer Science, Poznań, Poland,
e-mail: mkrzysko@amu.edu.pl


In this paper, we propose the principal components applicable in the case of multivariate repeated measures data under the following assumptions: (1) multivariate normality for the vector of observations xj, (2) Kronecker product structure of the positive definite covariance matrix Ω. Computational schemes for maximum likelihood estimates of required parameters are also given.


Principal component analysis; repeated measures data; Kronecker product covariance structure; maximum likelihood estimates