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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||摘要:||
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.
|Appears in Collections:||統計學研究所|
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