D-optimally Lack-of-Fit-Test-efficient Designs and Related Simple Designs
DOI:
https://doi.org/10.17713/ajs.v37i3&4.306Abstract
In practice it is often more popular to use a uniform than an optimal design for estimating the unknown parameters of a linear regression model. The reason is that the model can be checked by a uniform design but it cannot be checked by an optimal design in many cases. On the other hand, however, for important regression models a uniform design is not very efficient to estimate the unknown parameters. Therefore Bischoff and Millerproposed in a series of papers a compromise. It is suggested there to look for designs that are optimal with respect to a specific criterion in the class of designs that are efficient for lack-of-fit-tests. In this paper we consider the D-criterion and polynomial regression models. For polynomial regression models with degree larger than two D-optimally lack-of-fit-test-efficient designs are difficult to determine. Therefore, in this paper we determine easily to calculate and for estimating the parameters highly efficient designs that are
additionally lack-of-fit-test–efficient.
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