Estimation and incommutativity in mixed models

Dário Ferreira, Sandra Ferreira, Célia Nunes, Miguel Fonseca, Adilson Silva and João T. Mexia

Journal of Multivariate Analysis, 2017, vol. 161, issue C, 58-67

Abstract: In this paper we present a treatment for the estimation of variance components and estimable vectors in linear mixed models in which the relation matrices may not commute. To overcome this difficulty, we partition the mixed model in sub-models using orthogonal matrices. In addition, we obtain confidence regions and derive tests of hypothesis for the variance components. A numerical example is included. There we illustrate the estimation of the variance components using our treatment and compare the obtained estimates with the ones obtained by the ANOVA method. Besides this, we also present the restricted and unrestricted maximum likelihood estimates.

Keywords: Inference; Mixed models; Variance components (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:161:y:2017:i:c:p:58-67

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DOI: 10.1016/j.jmva.2017.07.002

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