Complex covariance structureoptimal sampling for an efficient estimation
- Itziar Irigoien (ed. lit.)
- Dae-Jin Lee (ed. lit.)
- Joaquín Martínez-Minaya (ed. lit.)
- María Xosé Rodríguez- Álvarez (ed. lit.)
Éditorial: Servicio Editorial = Argitalpen Zerbitzua ; Universidad del País Vasco = Euskal Herriko Unibertsitatea
ISBN: 978-84-1319-267-3
Année de publication: 2020
Pages: 410-413
Congreso: International Workshop on Statistical Modelling (35. 2020. Bilbao)
Type: Communication dans un congrès
Résumé
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.