Work

Construction of optimal multi-level supersaturated designs

Year: 2005

Type: article

Abstract: A supersaturated design is a design whose run size is not large enough for estimating all the main effects. The goodness of multi-level supersaturated designs can be judged by the generalized minimum aberration criterion proposed by Xu and Wu [Ann. Statist. 29 (2001) 1066–1077]. A new lower bound is derived and general construction methods are proposed for multi-level supersaturated designs. Inspired by the Addelman–Kempthorne construction of orthogonal arrays, several classes of optimal multi-level supersaturated designs are given in explicit form: Columns are labeled with linear or quadratic polynomials and rows are points over a finite field. Additive characters are used to study the properties of resulting designs. Some small optimal supersaturated designs of 3, 4 and 5 levels are listed with their properties.

Source: The Annals of Statistics

DOI: 10.1214/009053605000000688

Authors: Hongquan Xu, C. F. J. Wu

Institutions: University of California, Los Angeles, Georgia Institute of Technology

Language: English

Topic: Optimal Experimental Design Methods

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: bronze

Funder: National Science Foundation

Awards: Nonregular Designs: Classification, Optimality and Construction, Statistical Research in Drug Discovery and Development, Design and Analysis of Experiments for Screening, Optimization and Robustness