Computing optimal experimental designs with respect to a compound Bayes risk criterion
Abstract
We consider the problem of computing optimal experimental design on a finite design space with respect to a compound Bayes risk criterion, which includes the linear criterion for prediction in a random coefficient regression model. We show that the problem can be restated as constrained A-optimality in an artificial model. This permits using recently developed computational tools, for instance the algorithms based on the second-order cone programming for optimal approximate design, and mixed-integer second-order cone programming for optimal exact designs. We demonstrate the use of the proposed method for the problem of computing optimal designs of a random coefficient regression model with respect to an integrated mean squared error criterion.
Cite
@article{arxiv.1709.02317,
title = {Computing optimal experimental designs with respect to a compound Bayes risk criterion},
author = {Radoslav Harman and Maryna Prus},
journal= {arXiv preprint arXiv:1709.02317},
year = {2017}
}