Robust split-plot designs for model misspecification

• Autores: Chang Yun Lin
• Localización: Journal of quality technology: A quarterly journal of methods applications and related topics, ISSN 0022-4065, Nº. 50, 1, 2018, págs. 76-87
• Idioma: inglés
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• Resumen

  • Many existing methods for constructing optimal split-plot designs, such as D-optimal or A-optimal designs, focus only on minimizing the variance of the parameter estimates for the fitted model. However, the true model is usually more complicated; hence, the fitted model is often misspecified. If significant effects not included in the model exist, then the estimates could be highly biased. Therefore a good split-plot design should be able to simultaneously control the variance and the bias of the estimates. In this article, I propose a new method for constructing optimal split-plot designs that are robust under model misspecification. Four examples are provided to demonstrate that my method can produce efficient split-plot designs with smaller overall aliasing. Simulation studies are performed to verify that the robust designs I construct have high power, low false discovery rate, and small mean squared error.