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標題: Robust split-plot designs for model misspecification
作者: Chang-Yun Lin
關鍵字: A-efficiency;coordinate-exchange algorithm;D-efficiency;generalized least squares;loss function;mean squared error
出版社: Journal of Quality Technology
Project: JOURNAL OF QUALITY TECHNOLOGY 2018, VOL. 50, NO. 1, 76–87
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.
DOI: 10.1080/00224065.2018.1404325
Appears in Collections:統計學研究所

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