Abstract

A necessary characteristic of designs for deterministic computer simulations is that they avoid replication. This characteristic is also necessary for one-dimensional projections of the design, since it may turn out that only one of the design factors has any non-negligible effect on the response. Latin Hypercube designs have uniform one-dimensional projections are not efficient for fitting low order polynomials when there is a small error variance. D-optimal designs are very efficient for polynomial fitting but have substantial replication in projections. We propose a new class of designs that bridge the gap between Latin Hypercube designs and D-optimal designs. These designs guarantee a minimum distance between points in any one-dimensional projection. Subject to this constraint they are D-optimal for any pre-specified model.