Using a two-stage minimum aberration criterion to select optimal two-level fractional factorial designs

Weiming Ke, Cuirong Ren and Huitian Lu

In selecting 2^{m-p} designs when some of the two-factor interactions are important,
the key issues are to permit estimation of the main effects and important two-factor
interactions in a postulated model and to minimize the bias caused by the
other effects not included in the model. If the main effects need more protection
than the important two-factor interactions, we should rst minimize the bias of
the main effects, and then minimize the bias of the important two-factor interactions.
In this paper, a two-stage minimum aberration criterion is proposed to
minimize the bias of the main effects and that of the important two-factor interactions
sequentially. Searching for the best designs according to this criterion is
discussed and some results for designs of 16 and 32 runs are presented.

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