Gaona Barrera, Lisette M.

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Matrices de covarianza estructuradas en modelos con medidas repetidas
(2005) Gaona Barrera, Lisette M.; Macchiavelli, Raúl E.; College of Arts and Sciences - Sciences; Acuña, Edgar; Quintana, Julio; Department of Mathematics; Wessel Beaver, Linda
Many experiments in plants and animals, clinical trials and economic studies, have in common recording several measurements through time on the same experimental unit. These experiments use the mixed linear models with repeated measures to accommodate to the fixed effects of treatment and time, and the random effect of subject. An aspect of the specification of these models is the selection of the covariance structure of the errors. This study focuses on validating a methodology to select the covariance structure that better fits the data. For the validation of such methodology, simulated datasets are used under different covariance structures. Schwarz’s information criterion is used as the selection criterion of covariance structure and the results are evaluated in terms of the percentage of rejection of F-tests for estimated means. A relevant result is that Schwarz’s information criterion chooses the greater number of times the true covariance structure when it is AR(1) or ARH(1) with high correlation regardless of the value of the variance of subject, and when the model has matrices CS, CSH, ANTE(1) and TOEP, in which the variance of subject is not identifiable.