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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.
About the author
Chang-Yun Lin is an Associate Professor in the Department of Applied Mathematics and Institute of Statistics at the National Chung Hsing University in Taiwan. He received his Ph.D. degree from the Tsing Hua University in Taiwan in 2009. His research areas concern design of experiments, deep learning, Bayesian analysis, and genetic statistics.
Acknowledgments
The author gratefully acknowledges the helpful comments by the editor and referees.
Funding
This research was supported by the Ministry of Science and Technology of Taiwan (Grant no. MOST 105-2118-M-005-004-MY2).