Efficient designs are in high demand in practice for both computer and physical experiments. Existing designs (such as maximin distance designs and uniform designs) may have bad low-dimensional projections, which is undesirable when only a few factors are active. We propose a new design criterion, called uniform projection criterion, by focusing on projection uniformity. Uniform projection designs generated under the new criterion scatter points uniformly in all dimensions and have good space-filling properties in terms of distance, uniformity and orthogonality. We show that the new criterion is a function of the pairwise L1-distances between the rows, so that the new criterion can be computed at no more cost than a design criterion that ignores projection properties. We develop some theoretical results and show that maximin L1-equidistant designs are uniform projection designs. In addition, a class of asymptotically optimal uniform projection designs based on good lattice point sets are constructed. We further illustrate an application of uniform projection designs via a multidrug combination experiment.
Publication:
The Annals of Statistics, 2019, Vol. 47, No. 1, 641–661
Author:
Fasheng Sun
KLAS and School of Mathematics and Statistics, Northeast Normal University, Changchun, Jilin 130024, China
Yaping Wang
School of Statistics, East China Normal University, Shanghai 200241, China
Hongquan Xu
Department of Statistics, University of California, Los Angeles, CA 90095, USA