学术报告：Mixed Global Sensitivity Analysis

报告时间：5月28日（星期三）下午14:00-15:00

报告地点：沙河校区，二教110

主持人：盖玉洁 教授

报告人：王晓迪，中央财经大学，副教授

报告摘要：Global sensitivity analysis (GSA) plays a pivotal role in elucidating the structural dependency of a black-box model on its input parameters. In recent years, a new class of models with inhomogeneous inputs has emerged in designed experiments for quality improvement, featuring “quantitative”and “permutable”factors that exert different influences on the output of interest. Traditional GSA methods designed for single-subject analysis are not applicable to these quantity-permutation (QP) models. To address this challenge, we propose an innovative method, called mixed global sensitivity analysis (MGSA), for extracting key features in QP models. The MGSA method splits the model into two parts. One part is used to implement ANOVA decomposition to identify influential quantitative factors, while the other part undergoes symmetrical decomposition to learn about the symmetric pattern induced by the permutable factors. Theoretical properties of the proposed method are studied, and optimal designs for efficient data collection are developed. We demonstrate the effectiveness of the BGSA method by explaining different models and solving a practical problem.

报告人简介：中央财经大学统计与数学学院副教授、中国现场统计学会试验设计分会理事、全国工业统计学教学研究会青年统计学家协会理事。在Applied mathematical modelling, Computational Statistics & Data Analysis, Knowledge-Based Systems等统计学和计算机科学期刊发表论文30余篇。主持及参与国家自然科学基金项目4项。

撰稿人：刘洁

审稿人：邓露