Robust split-plot designs for model misspecification. Issue 1 (2nd January 2018)

• Record Type: Journal Article
• Authors: Lin, Chang-Yun
• Is Part Of: Journal of quality technology. Volume 50:Issue 1(2018)
• Page Start: 76
• Page End: 87
• Publication Date: 2018-01-02
• Subjects: A-efficiency -- coordinate-exchange algorithm -- D-efficiency -- generalized least squares -- loss function -- mean squared error; Quality control -- Periodicals; Qualité -- Contrôle -- Périodiques; Quality control; Quality control; Periodicals; 620.0045
• DOI: 10.1080/00224065.2018.1404325
• Languages: English
• ISSNs: 0022-4065

Abstract

ABSTRACT: 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.