Model-robust designs for split-plot experiments

Byran J. Smucker, Enrique del Castillo and James L. Rosenberger

Computational Statistics & Data Analysis, 2012, vol. 56, issue 12, 4111-4121

Abstract: Split-plot experiments are appropriate when some factors are more difficult and/or expensive to change than others. They require two levels of randomization resulting in a non-independent error structure. The design of such experiments has garnered much recent attention, including work on exact D-optimal split-plot designs. However, many of these procedures rely on the a priori assumption that the form of the regression function is known. We relax this assumption by allowing a set of model forms to be specified, and use a scaled product criterion along with an exchange algorithm to produce designs that account for all models in the set. We include also a generalization which allows weights to be assigned to each model, though they appear to have only a slight effect. We present two examples from the literature, and compare the scaled product designs with designs optimal for a single model. We also discuss a maximin alternative.

Keywords: D-optimality; Exact experimental design; Model-robust; Split-plot; Maximin (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:12:p:4111-4121

DOI: 10.1016/j.csda.2012.03.010

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