Summary of effect aliasing structure (SEAS): new descriptive statistics for factorial and supersaturated designs

TL;DR

The paper introduces SEAS, a new set of descriptive statistics for evaluating the aliasing structure in supersaturated and fractional factorial designs, providing more detailed insights than traditional methods.

Contribution

It develops the SEAS framework with three criteria (MAP) for better assessment of aliasing, and demonstrates its application in design comparison and factor assignment.

Findings

SEAS offers detailed aliasing insights beyond traditional criteria.

The MAP criteria effectively differentiate between complex designs.

SEAS can guide optimal factor-to-column assignments in experimental designs.

Abstract

In the assessment and selection of supersaturated designs, the aliasing structure of interaction effects is usually ignored by traditional criteria such as $E(s^2)$-optimality. We introduce the Summary of Effect Aliasing Structure (SEAS) for assessing the aliasing structure of supersaturated designs, and other non-regular fractional factorial designs, that takes account of interaction terms and provides more detail than usual summaries such as (generalized) resolution and wordlength patterns. The new summary consists of three criteria, abbreviated as MAP: (1) Maximum dependency aliasing pattern; (2) Average square aliasing pattern; and (3) Pairwise dependency ratio. These criteria provided insight when traditional criteria fail to differentiate between designs. We theoretically study the relationship between the MAP criteria and traditional quantities, and demonstrate the use of SEAS for comparing some example supersaturated designs, including designs suggested in the literature. We also propose a variant of SEAS to measure the aliasing structure for individual columns of a design, and use it to choose assignments of factors to columns for an $E(s^2)$-optimal design.