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报告题目:G-optimal Designs for Hierarchical Linear Models: an Equivalence Theorem and a Nature-inspired Meta-heuristic Algorithm

报告人:岳荣先  教授

报告摘要:Hierarchical linear models are widely used in many research disciplines and estimation issues for such models are generally well addressed. Design issues are relatively much less discussed for hierarchical linear models but there is an increasing interest as these models grow in popularity. We consider G-optimality for predicting individual parameters in such models and establishes an equivalence theorem for confirming G-optimality of an approximate design. Because the criterion is non-differentiable and requires solving multiple nested optimization problems, it is much harder to find and study G-optimal designs analytically. We propose a nautre-inspired meta-heuristic algorithm called competitive swarm optimizer (CSO) to generate G-optimal designs for linear mixed models with different means and covariance structures.


报告地点:腾讯会议  ID: 654 813 775


报告人简介:岳荣先,上海师范大学教授,博士生导师,兼任上海师范大学学术委员会委员、上海师范大学学术伦理与道德委员会主任、数理学院学术委员会主任。 长期从事试验设计与拟蒙特卡洛方法的研究,主持完成和在研国家自然科学基金面上项目5项,主持完成教育部高校博士点专项科研基金项目2项。研究成果先后在《SIAM J. Numer. Anal.》《Math. Comput.》《J. Complexity》《Statistica Sinica》《Comput. Statist. Data Anal.》《J. Multivariate Anal.》《J. Stat. Plan. Inference》等国际学术期刊发表。兼任中国数学会均匀设计分会副理事长、中国现场统计研究会试验设计分会副理事长、中国现场统计研究会生存分析分会副理事长。