Privacy sets for constrained space-filling
Eva Benková, Radoslav Harman, Werner G. Müller · Journal of Statistical Planning and Inference · 2015
Utilizing a typology for space filling into what we call “soft” and “hard” methods, we introduce the central notion of “privacy sets” for dealing with the latter. This notion provides a unifying framework for standard designs without replication, Latin hypercube designs, and Bridge designs, among many others. We introduce a heuristic algorithm based on privacy sets and compare its performance on some well-known examples. For instance, we demonstrate that for the computation of Bridge designs this algorithm performs significantly better than the state-of-the-art method. Moreover, the application of privacy sets is not restricted to cuboid design spaces and promises improvements for many other situations.