Complex covariance structure: optimal sampling for an efficient estimation
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Autores: Juan Manuel Rodríguez Díaz
- Localización: Proceedings of the 35th International Workshop on Statistical Modelling : July 20-24, 2020 Bilbao, Basque Country, Spain / Itziar Irigoien Garbizu (ed. lit.) , Dae-Jin Lee (ed. lit.) , Joaquín Martínez Minaya (ed. lit.), María Xosé Rodríguez Álvarez (ed. lit.), 2020, ISBN 978-84-1319-267-3, págs. 410-413
- Idioma: inglés
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Resumen
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.
