Advances in Industry Statistics

Organizer and Chair: Arman Sabbaghi, Associate Professor of Statistics

Speakers

Lin Wang, Assistant Professor of Statistics, Purdue University
Frederick Kin Hing Phoa, Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

Speaker	Title
Lin Wang	Generating Space-filling Designs for Large Computer Experiments
Abstract: Space-filling designs are commonly used in controlled experiments for investigating complex simulation systems. Latin hypercube design is a popular type of space-filling design because it studies as many levels as the design size for each variable and therefore achieves one-dimensional uniformity. In this talk, I will introduce a series of new methods for generating large and high-dimensional Latin hypercube designs. The generated designs are shown to be optimal under the maximin distance criterion and have small pairwise correlations between variables. When those many levels in a Latin hypercube design are not needed to learn the simulation system, the proposed methods can also be used to generate space-filling designs with less balanced levels.
Frederick Kin Hing Phoa	A systematic design construction and analysis for cost-efficient order-of-addition experiment
Abstract: In this work, we propose a systematic design construction method for cost-efficient order-of-addition (OofA)experiments, and its corresponding statistical models for analyzing experimental results. In specific, our designs take the effects of two successive treatments into consideration. Each pair of level settings from two different factors in our design matrix appears exactly once to achieve cost-efficiency. Compared to designs in recent studies of OofA experiments, our design is capable of conducting experiments of one or more factors, so practitioners can insert a placebo, or choose different doses as level settings when our design is used as their experimental plans. We show an experimental analysis based on our design results in better performance than those based on the minimal-point design and Bayesian D-optimal design with the pairwise-order modeling in terms of identifying the optimal order.