Resumen de Complex covariance structure: optimal sampling for an efficient estimation
In most scienti c disciplines models are proposed in order to describe di erent phenomena. In these models, the behavior of one or more variables is observed, trying to link these responses with other factors or covariates that may (at least partially) explain the former ones. An usual assumption for these observations is that they are independent, and many procedures have been developed for all kind of studies when assuming uncorrelated observations. However, it is clear that this assumption cannot be maintained for many real problems; several covariance structures can arise, and even appear combined, increasing the complexity of the models. Di erent situations will be examined, and some solutions for obtaining the 'best' designs for estimation of the parameters will be proposed employing optimal experimental design techniques.
