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Orientador(es)
Resumo(s)
The models constituting a multiple model will correspond to d treatments of a base design.
Using a classic result on cumulant generation function we show how to obtain least square estimators for cumulants and generalized least squares estimators for vectors \beta, l=1,...,d, in the individual models.
Next we carry out ANOVA-like analysis for the action of the factors in the base design. This is possible since the estimators \tilde{\beta }(l) of \beta (l). l=1,...,d, have, approximately, the same covariance matrix. The eigenvectors of that matrix will give the principal estimable functions \epsilon_{i}^{\top} \beta (l) i=1,...,k, l=1,...,d, for the individual models. The ANOVA-like analysis will consider homologue components on principal estimable functions.
To apply our results we assume the factors in the base design to have fixed effects.
Moreover if w=1, and Z(1) has covariance matrix \sigma^{2} \m I_{n}, our treatment generalizes that previously given for multiple regression designs. In them we have a linear regression for each treatment of a base design. We then study the action of the factors on that design on the vectors \beta(l), l=1,...,d. An example of application of the proposed methodology is given.
Descrição
Palavras-chave
ANOVA Cumulants Mixed Models
Contexto Educativo
Citação
Patrícia Antunes, Sandra S. Ferreira, Dário Ferreira and João T. Mexia (2020). Multiple Additive Models. Communications in Statistics – Theory and Methods
Editora
Marcel Dekker Inc.