On maximin designs for correlated observations

Kurzfassung/Abstract

In the linear model, we consider the problem of finding optimal or efficient designs with respect to the $D$-criterion when the covariance matrix is an unknown element of a class $\cal C$. In general, designs that are efficient for each $C \in {\cal C}$ do not exist. Therefore, maximin designs are of interest. These designs maximize the minimal efficiency where the minimum is taken over all possible covariance matrices and the maximum is taken over all feasible designs. Efficient maximin designs are derived for tridiagonal covariance matrices.