by Alonso Cabrera, J.; Schwabe, R..
Series: 2015-17, Preprints
Optimal experimental designs are developed for linear regression
models with both qualitative and quantitative factors of in
uence. In particular we generate a characterization of optimal designs for ran-
dom blocked regression experiments where under few assumptions,
this characterization allows to find the weights of the optimal de-
sign analytically by means of convex optimization. It is worth-while
noting that the optimal weights depend on the ratio of the variance
components. However, in this context, we show that in practical ap-
plications limiting optimal designs show a high effciency, when the
variance ratio approaches zero or infinity.
D-optimal designs, random block eects, partial interactions, qualitative and quantitative factors, product type design.