Joint Statistics / Biostatistics Seminar: STA/BST 290
DATE: Tuesday, April 22nd, 2014
TIME: 4:10pm (refreshments at 3:30pm, MSB 4110)
LOCATION: Mathematical Sciences Building 1147
SPEAKER: Weng Kee Wong, Dept of Biostatistics, Fielding School of Public Health, UCLA
TITLE: Using Animal Instincts to Find Efficient Experimental Designs
ABSTRACT: Experimental study costs are rising and it is important to use minimal resources to make statistical inference with maximal precision. Optimal design theory and ideas are increasingly applied to address design issues in an expanding field of diverse disciplines that include biomedicine, biochemistry, education, agronomy, manufacturing industry, toxicology and food science, to name a few.
I first present a brief overview of optimal design methodology and recent advances in the field. Particle swarm optimization (PSO) is then introduced to find optimal designs for potentially any model and any design criterion. The method works quite magically and frequently finds the optimal solution or a nearly optimal solution for an optimization problem in a very short time. There is virtually no explicit assumption required for the method to perform well and the user only needs to input a few easy tuning parameters in the PSO algorithm. Using popular models from the biopharmaceutical sciences as examples, I show how PSO searches for different types of optimal experimental designs for dose response studies, including mini-max types of optimal designs where effective algorithms to find such designs have remained stubbornly elusive until now. Applications of PSO to solve other complex statistical optimization problems are also possible and I will very briefly discuss them.