Some optimal criteria of model-robustness for two-level non-regular fractional factorial designs

TL;DR

This paper introduces optimal criteria for assessing the robustness of non-regular two-level fractional factorial designs, focusing on minimizing off-diagonal elements in the information matrix and using generalized minimum aberration.

Contribution

The paper proposes new optimal criteria based on information matrix properties and generalized minimum aberration for evaluating model robustness in non-regular factorial designs.

Findings

Criteria effectively evaluate design robustness.

Empirical studies demonstrate the criteria's applicability.

Designs optimized by the criteria show improved robustness.

Abstract

We present some optimal criteria to evaluate model-robustness of non-regular two-level fractional factorial designs. Our method is based on minimizing the sum of squares of all the off-diagonal elements in the information matrix, and considering expectation under appropriate distribution functions for unknown contamination of the interaction effects. By considering uniform distributions on symmetric support, our criteria can be expressed as linear combinations of $B_s(d)$ characteristic, which is used to characterize the generalized minimum aberration. We give some empirical studies for 12-run non-regular designs to evaluate our method.