A0273
Title: ForLion: A new algorithm for D-optimal designs under general parametric statistical models with mixed factors
Authors: Yifei Huang - University of Illinois at Chicago (United States)
Keren Li - University of Alabama at Birmingham (United States)
Abhyuday Mandal - University of Georgia (United States)
Jie Yang - University of Illinois at Chicago (United States) [presenting]
Abstract: The aim is to address the problem of designing an experimental plan with both discrete and continuous factors under fairly general parametric statistical models. A new algorithm, named ForLion, is proposed to search for locally optimal approximate designs under the D-criterion. The algorithm performs an exhaustive search in a design space with mixed factors while keeping high efficiency and reducing the number of distinct experimental settings. Its optimality is guaranteed by the general equivalence theorem. The relevant theoretical results are presented for multinomial logit models (MLM) and generalized linear models (GLM), and the superiority of the algorithm is demonstrated over state-of-the-art design algorithms using real-life experiments under MLM and GLM. Simulation studies show that the ForLion algorithm could reduce the number of experimental settings by 25\% or improve the relative efficiency of the designs by 17.5\% on average. The algorithm can help the experimenters reduce the time cost, the usage of experimental devices, and thus the total cost of their experiments while preserving high efficiencies of the designs.
