DEVACOE

Design of experiments for variance component estimation

Coordinatore	UNIVERSITEIT ANTWERPEN    more  Organization address address: PRINSSTRAAT 13 city: ANTWERPEN postcode: 2000 contact info Titolo: Prof. Nome: Peter Cognome: Goos Email: send email Telefono: +32 3 265 4059 Fax: +32 3 265 48 17
Nazionalità Coordinatore	Belgium [BE]
Totale costo	153˙400 €
EC contributo	153˙400 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2009-IEF
Funding Scheme	MC-IEF
Anno di inizio	2010
Periodo (anno-mese-giorno)	2010-04-01   -   2012-01-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITEIT ANTWERPEN  Organization address address: PRINSSTRAAT 13 city: ANTWERPEN postcode: 2000 contact info Titolo: Prof. Nome: Peter Cognome: Goos Email: send email Telefono: +32 3 265 4059 Fax: +32 3 265 48 17	BE (ANTWERPEN)	coordinator	153˙400.00

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 Obiettivo del progetto (Objective)

'The envisaged achievement of this project is to perform groundbreaking work in the optimal design of experiments for estimating variance components in random effects models and for the joint estimation of fixed effects and variance components in mixed effects models, in general, and split-plot, strip-plot and split-split-plot models, in particular. The design of experiments for an efficient estimation of variance components is one of the remaining challenges in the field of optimal experimental design, so that the successful completion of this project would thus be a major breakthrough in statistical design of experiments. In Work Package 1, the focus is on the derivation of analytical expressions for the information matrices for random effects models and linear mixed models. This work will be based on the state-of-the-art restricted maximum likelihood estimation of the variance components. This is unlike the limited amount of published work in this research area, which is based on maximum likelihood or ANOVA-based estimation of the variance components. The theoretical results from Work Package 1 will then be used to tackle two challenging unanswered research questions in industrial statistics. First, in Work Package 2, we will deal with the optimal design of split-plot, strip-plot and split-split-plot experiments for estimating fixed effects as well as variance components. These designs are highly popular experimental plans for research about innovation in industry because they are cost-efficient, but the estimation of the variance components has been entirely ignored in all of the published work on their construction. Second, in Work Package 3, we will deal with the optimal setup of measurement capability studies, also known as gauge repeatability and reproducibility studies (also referred to as R&R studies). This subject is also an unexplored research area.'