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

Mirosław Krzysko; Tadeusz Smiałowski; Waldemar Wołynski

doi:10.2478/bile-2014-0008

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

Biometrical Letters

Volume 51 (2014): Issue 2 (December 2014)

By: Mirosław Krzysko, Tadeusz Smiałowski and Waldemar Wołynski

Open Access

|Dec 2014

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.

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Articles in this issue

DOI: https://doi.org/10.2478/bile-2014-0008 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811

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Language: English

Page range: 103 - 114

Published on: Dec 20, 2014

Published by: Polish Biometric Society

In partnership with: Paradigm Publishing Services

Publication frequency: 2 issues per year

Keywords:

multivariate repeated measures data (doubly multivariate data),

Kronecker product covariance structure,

maximum likelihood estimates,

mixed MANOVA model,

principal component analysis

Related subjects:

Life sciences,

Bioinformatics,

Life sciences, other,

Mathematics,

Probability and statistics,

Applied mathematics

© 2014 Mirosław Krzysko, Tadeusz Smiałowski, Waldemar Wołynski, published by Polish Biometric Society
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 51 (2014): Issue 2 (December 2014)