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,华东师范大学金沙集团app金沙集团app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,应用金沙集团app,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: 金沙集团app与数据科学金沙集团app会议室106