2 research outputs found
Estimation and incommutativity in mixed models
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.info:eu-repo/semantics/publishedVersio
Estimation in additive models and ANOVA-like applications
A well-known property of cumulant generating function is used to
estimate the first four order cumulants, using least-squares estimators.
In the case of additive models, empirical best linear unbiased
predictors are also obtained. Pairs of independent and identically
distributed models associated with the treatments of a base design
are used to obtain unbiased estimators for the fourth-order cumulants.
An application to real data is presented, showing the good
behaviour of the least-squares estimators and the great flexibility of
our approach.info:eu-repo/semantics/publishedVersio