A generalization of the alias matrix

Abstract

The investigation of aliases or biases is important for the interpretation of the results from factorial experiments. For two-level fractional factorials this can be facilitated through their group structure. For more general arrays the alias matrix can be used. This tool is traditionally based on the assumption that the error structure is that associated with ordinary least squares. For situations where that is not the case, we provide in this article a generalization of the alias matrix applicable under the generalized least squares assumptions. We also show that for the special case of split plot error structure, the generalized alias matrix simplifies to the ordinary alias matrix.

Acknowledgment

The Eugene M. Isenberg Program for Technology Management, The Isenberg School of Management at University of Massachusetts, Amherst, supported the research by Soren Bisgaard.