论文标题
评估与相关观测值的C-最佳实验设计的组合优化算法
Evaluation of Combinatorial Optimisation Algorithms for c-Optimal Experimental Designs with Correlated Observations
论文作者
论文摘要
我们展示了如何将组合优化算法应用于识别C-在实验单元之间和内部可能存在相关性并评估相关算法的性能时，识别C-最佳实验设计的问题。我们假设数据生成过程是一种广义的线性混合模型，并表明C-最佳设计准则是可符合一组简单最小化算法的单调超模块化函数。我们评估了三种相关算法的性能：本地搜索，贪婪搜索和反向贪婪搜索。我们表明，本地和反向贪婪的搜索提供了可比的性能，而最差的设计输出具有差异$ <10 \％$ $ $ $ $ $ $ $ $ $ $ $比最佳设计，跨越一系列协方差结构。我们表明，这些算法比产生重量以放置在实验单元上的乘法方法的性能效果或更好。我们将这些算法扩展到识别Moole-bust C-Optimal设计。
We show how combinatorial optimisation algorithms can be applied to the problem of identifying c-optimal experimental designs when there may be correlation between and within experimental units and evaluate the performance of relevant algorithms. We assume the data generating process is a generalised linear mixed model and show that the c-optimal design criterion is a monotone supermodular function amenable to a set of simple minimisation algorithms. We evaluate the performance of three relevant algorithms: the local search, the greedy search, and the reverse greedy search. We show that the local and reverse greedy searches provide comparable performance with the worst design outputs having variance $<10\%$ greater than the best design, across a range of covariance structures. We show that these algorithms perform as well or better than multiplicative methods that generate weights to place on experimental units. We extend these algorithms to identifying moole-robust c-optimal designs.
