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

Yu Shi (), Zizhao Zhang () and Weng Kee Wong ()
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Yu Shi: Department of Biostatistics, University of California at Los Angeles
Zizhao Zhang: Department of Biostatistics, University of California at Los Angeles
Weng Kee Wong: Department of Biostatistics, University of California at Los Angeles

Journal of Statistical Distributions and Applications, 2019, vol. 6, issue 1, 1-17

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.

Keywords: Bayesian design; Design efficiency; Generalized linear model; Metaheuristic algorithms (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (2)

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DOI: 10.1186/s40488-019-0092-4

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