2025/08/11 by Yeh, Chi-Kuang, Wong, Weng Kee, Zhou, Julie
#FOS: Computer and information sciences #Methodology (stat.ME)
paper · doi:10.48550/arxiv.2508.08445
Group testing, or pooled sample testing, is an active research area with increasingly diverse applications across disciplines. This paper considers design issues for group testing problems when a statistical model, an optimality criterion and a cost function are given. We use the software CVX to find designs that best estimate all or some of the model parameters (D -, Ds -, A-optimality) when there is one or more objectives in the study. A novel feature is that we include maximin types of optimal designs, like E-optimal designs, which do not have a differentiable criterion and have not been used in group testing problems before, or, as part of a criterion in a multi-objective design problem. When the sample size is large, we search for optimal approximate designs; otherwise, we find optimal exact designs and compare their robustness properties under a variation of criteria, statistical models, and cost functions. We also provide free user-friendly CVX sample codes to facilitate implementation of our proposed designs and amend them to find other types of optimal designs, such as, robust E-optimal designs.