Experimental Designs for Model Discrimination
研究了能同时兼顾多个目标的实验设计方法，包括最优扩充设计、混合准则评估和约束优化，并以区分二次与三次多项式模型为例给出样本设计。
Abstract We present designs that perform well for several objectives simultaneously. Three different approaches are discussed: to augment a given design in an optimal way, to evaluate a mixture of the various criteria, and to optimize one objective subject to achieving a prescribed efficiency level for the others. Our sample designs are for the situation of discriminating between a second- and third-degree polynomial fit, under the D-criterion and geometric mixtures of D-criteria.