The Sequential Generation of $D$-Optimum Experimental Designs

Abstract

It is possible to obtain convergence to a $D$-optimum measure, as defined by Kiefer and Wolfowitz, by successively adding points to a given initial experimental design. The points added correspond to points of maximum variance of the usual least squares estimate of the response mean for the particular regression model at each stage. A new bound is given for the generalized variances involved and an example is worked out.