Abstract
This chapter focuses on model robust designs. The design of an experiment is based on an assumed model that is usually believed to be a reasonable approximation to the true model. If the assumed model is inadequate, a classical optimal design based on the assumed model may provide significantly biased information about the true response. In other words, the optimal design for the assumed model can actually be a bad design for the true model, and therefore, is not a model robust design. A good model robust design must (1) allow one to fit the assumed model, (2) detect the model inadequacy when the fitted model is a poor approximation to the true model, and (3) allow one to make reasonable efficient inferences concerning the assumed model when the assumed model is adequate. A reasonable model robust design for detecting model adequacy is unlikely to be an optimal design for the assumed model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
