Particle swarm based algorithms for finding locally and Bayesian D-optimal designs

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2019
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Abstract
Abstract When a model-based approach is appropriate, an optimal design can guide how to collect data judiciously for making reliable inference at minimal cost. However, finding optimal designs for a statistical model with several possibly interacting factors can be both theoretically and computationally challenging, and this issue is rarely discussed in the literature. We propose nature-inspired metaheuristic algorithms, like particle swarm optimization (PSO) and its variants, to solve such optimization problems. We demonstrate that such techniques, which are easy to implement, can find different types of optimal designs for models with several factors efficiently. To facilitate use of such algorithms, we provide computer codes to generate tailor made optimal designs and evaluate efficiencies of competing designs. As applications, we apply PSO and find Bayesian optimal designs for Exponential models useful in HIV studies and re-design a car-refuelling study for a Logistic model with ten factors and some interacting factors.
Reference Key
shi2019particlejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Shi, Yu;Zhang, Zizhao;Wong, Weng Kee;
Journal journal of statistical distributions and applications
Year 2019
DOI
DOI not found
URL
http://link.springer.com/article/10.1186/s40488-019-0092-4
Keywords
Engineering (General). Civil engineering (General) Information technology Technology Science (General) neurology. diseases of the nervous system social sciences (general) special aspects of education mathematics electronic computers. computer science industrial engineering. management engineering computer software probabilities. mathematical statistics

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