Estimation in mixed-effects functional ANOVA models

Functional mixed-effects models are very useful in analyzing data. In this paper, we consider a functional mixed-effects model, where the observations are the real functions, and derive the maximum likelihood estimators of the functional parameters and variance components. The properties of the maxi...

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Bibliographic Details
Published inJournal of multivariate analysis Vol. 133; pp. 346 - 355
Main Authors Rady, E.A., Kilany, N.M., Eliwa, S.A.
Format Journal Article
LanguageEnglish
Published New York Elsevier Inc 01.01.2015
Taylor & Francis LLC
Subjects
Data analysis
Functional analysis of variance
Linear mixed-effects models
Mathematical models
Maximum likelihood estimator
Maximum likelihood method
Parameter estimation
Studies
Variance analysis
secondary
Maximum likelihood estimator
Linear mixed-effects models
Functional analysis of variance
primary
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Summary:Functional mixed-effects models are very useful in analyzing data. In this paper, we consider a functional mixed-effects model, where the observations are the real functions, and derive the maximum likelihood estimators of the functional parameters and variance components. The properties of the maximum likelihood estimators are also investigated.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2014.09.020