Sliced Space-filling Designs
Space-filling
designs are becoming increasingly popular in computer experiments, numerical
integration and stochastic optimization. The standard framework for
space-filling designs assumes that all the factors are quantitative. We propose an approach to constructing a
new type of design, called sliced space-filling design, to accommodate both
qualitative and quantitative factors. It starts with constructing a Latin
hypercube design based on a special orthogonal array for the quantitative
factors and then partitions the design into groups corresponding to different
level combinations of the qualitative factors. The points in each group have
good space-filling properties. Sampling properties of the constructed designs
will be discussed. Sliced space-filling designs are useful for computer
experiments with qualitative and quantitative factors, model ensembles and
cross-validation.