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

20 Pages Posted: 5 Oct 2014

See all articles by Xu Xu

Xu Xu

University of Wisconsin - Madison

Peter Qian

University of Wisconsin - Madison

Qing Liu

University of Wisconsin-Madison

Date Written: August 31, 2014

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.

Keywords: Computer experiments; Design of experiments; Orthogonal array based Latin hypercube desings; Data pooling.

JEL Classification: C90

Suggested Citation

Xu, Xu and Qian, Peter and Liu, Qing, Samurai Sudoku-Based Space-Filling Designs for Multi-Source Inference (August 31, 2014). Available at SSRN: https://ssrn.com/abstract=2505445 or http://dx.doi.org/10.2139/ssrn.2505445

Xu Xu

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Peter Qian

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

Qing Liu (Contact Author)

University of Wisconsin-Madison ( email )

United States
608-263-9298 (Phone)

HOME PAGE: http://bus.wisc.edu/faculty/qing-liu

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