报告时间： 2022-11-16 09:30——2022-11-16 10:30

报告地点： 腾讯会议： 635248484

报告题目： Assessment and Visualization of Model Selection Uncertainty

报告人：李扬  教授

主办单位： 数据科学与人工智能学院  数理统计研究中心

【专家简介】

李扬，中国人民大学统计学院教授、博士生导师，统计咨询研究中心主任；国际统计学会推选会员、中国现场统计研究会常务理事、中国商业统计学会副会长、北京生物医学统计与数据管理研究会监事长；主要从事相关型数据分析、模型选择与不确定性评价、潜变量建模、临床试验设计等领域研究，承担国家自然科学基金面上项目、全国统计科学研究重大项目等科研项目二十余项，发表国内外期刊研究论文七十余篇。

【讲座摘要】

Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. To this goal, the concept of model confidence bounds (MCB) in the context of nested model is introduced. MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence, whose width and composition can be the measure of the model selection uncertainty. Several graphical tools are further proposed. These include the model uncertainty curve (MUC), G-plots, H-plots, and model selection deviation (MSD), to visualize the distribution of the selected model and the variability of model selection. The proposed assessment method and visualization tools enable the practitioner to evaluate the overall model selection uncertainty and to compare different model selection procedures. Monte Carlo simulations and the real data examples confirm the validity and illustrate the advantages of the proposed method.

撰稿：孙晓霞   审核：富宇     单位：数据科学与人工智能学院