Title: Uniform designs of experiments with mixtures under the criterion mean L1-distance and a new approach to Scheffé-type designs

Abstract: Mixture experiments analyze how changes in component proportions impact the response variable within the experimental region of a simplex. This paper introduces a new criterion, named the mean L1-distance (ML1D) criterion, for constructing uniform designs in mixture experiments. This criterion allows flexibility in point size and showcases a more uniform pattern within the experimental region. We also explore the optimal Scheffé-type simplex-lattice designs under the ML1D criterion. An interesting discovery is that the uniform mixture designs and the optimal Scheffé-type simplex-lattice designs are connected. For a two-component mixture design, these two types of designs are proven to be equivalent. For more than two-component mixture designs, numerical equivalences between the two designs are observed. These findings strengthen the rationale for users to adopt these designs in mixture experiments for modeling and prediction.

简介：

王亚平，华东师范大学统计学院教授，博士生导师，主要研究方向为试验设计与分析，计算机试验，响应曲面设计，应用统计，在Annals of Statistics、Journal of the American Statistical Association、Biometrika、《中国科学》等期刊发表20余篇论文，入选中国科协青年人才托举工程、国家高层次青年人才计划。

报告时间：2024年9月12号 16：00-17：00

地点： 统计与数据科学学院会议室106