Samurai Sudoku-Based Space-Filling Designs for Multi-Source Inference

Abstract

Pooling data from multiple sources plays an increasingly vital role in today's world. We propose a new type of design, called a Samurai Sudoku-based space-filling design to address this issue. Such a design is an orthogonal array based Latin hypercube design with the following attractive properties: (1) the complete design achieves attractive uniformity in both univariate and bivariate margins; (2) it can be divided into groups of subdesigns with overlaps such that each subdesign achieves maximum uniformity in both univariate and bivariate margins; (3) each of the overlaps achieves maximum uniformity in both univariate and bivariate margins. Examples are given to illustrate the properties of the proposed design, and to demonstrate the advantages of using the proposed design for pooling data from multiple sources.