讲座预告：王亚平：Uniform designs of experiments with mixtures under the criterion mean L1-distance and a new approach to Scheffé-type designs

报告题目：Uniform designs of experiments with mixtures under the criterion mean L1-distanceand a new approach to Scheffé-type designs

报告人：王亚平（华东师范大学统计学院教授）

报告时间：2024年12月19日 10:00-11:30

报告地点：文波智慧教室203

摘要：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.

报告人简介: 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。