AI Article Synopsis
- The optimal design methods for nonlinear models rely on true but unknown parameter values, and developing robust designs is important but challenging.
- Criteria like ED optimality aim to enhance robustness but can be computationally heavy and less effective at the extremes of parameter distributions.
- The text proposes two new criteria based on evaluating the Fisher information matrix at specific percentiles and compares their performance against existing robust design criteria.
Article Abstract
Optimal design methods for nonlinear models are dependent on the true but unknown parameter values. Criteria for developing designs that are robust to the choice of parameter values such as ED optimality have been proposed. However, these criteria are computationally intensive and can perform poorly at extremes of the prior parameter distribution. Two different criteria are proposed. Both involve evaluation of the determinant of the Fisher information matrix over models formed at various combinations of the 2.5th and 97.5th percentiles of the parameter space. The performance of the proposed optimality criteria is compared to two existing robust optimal design criteria.
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| http://dx.doi.org/10.1080/10543401003618918 | DOI Listing |
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