Chen Ping-Yang, Chen Ray-Bing, Tung Heng-Chin, Wong Weng Kee
Department of Statistics, National Cheng-Kung University, Tainan, 70101, Taiwan.
Department of Biostatistics, Fielding School of Public Health, UCLA, Los Angeles, CA 90095-1772, USA.
Chemometr Intell Lab Syst. 2017 Oct 15;169:79-86. doi: 10.1016/j.chemolab.2017.08.009. Epub 2017 Sep 6.
Locally optimal designs for nonlinear models require a single set of nominal values for the unknown parameters. An alternative is the maximin approach that allows the user to specify a range of values for each parameter of interest. However, the maximin approach is difficult because we first have to determine the locally optimal design for each set of nominal values before maximin types of optimal designs can be found via a nested optimization process. We show that particle swarm optimization (PSO) techniques can solve such complex optimization problems effectively. We demonstrate numerical results from PSO can help find, for the first time, formulae for standardized maximin -optimal designs for nonlinear model with 3 or 4 parameters on the compact and nonnegative design space. Additionally, we show locally and standardized maximin -optimal designs for inhibition models are not necessarily supported at a minimum number of points. To facilitate use of such designs, we create a web-based tool for practitioners to find tailor-made locally and standardized maximin optimal designs.
非线性模型的局部最优设计需要为未知参数设定一组标称值。另一种方法是极大极小方法,它允许用户为每个感兴趣的参数指定一个值范围。然而,极大极小方法很困难,因为在通过嵌套优化过程找到极大极小类型的最优设计之前,我们首先必须为每组标称值确定局部最优设计。我们表明,粒子群优化(PSO)技术可以有效地解决此类复杂的优化问题。我们展示了PSO的数值结果首次有助于找到在紧凑且非负设计空间上具有3个或4个参数的非线性模型的标准化极大极小最优设计公式。此外,我们表明抑制模型的局部和标准化极大极小最优设计不一定在最少数量的点上得到支持。为了便于使用此类设计,我们为从业者创建了一个基于网络的工具,以找到量身定制的局部和标准化极大极小最优设计。