题目：Variational estimation for multidimensional graded response model

报告人：徐平峰 教授（东北师范大学前沿交叉研究院）

摘要：Likert-type items with ordinal responses are frequently utilized in tests to assess multiple latent traits. The multidimensional graded response model (MGRM) is the preferred model for describing the relationship between these ordinal items and latent traits. In this paper, we propose a novel Gaussian variational expectation maximization (GVEM) method for parameter estimation in MGRM. Rather than relying on direct numerical approximations for intractable integrals over multidimensional latent traits, our GVEM employs a carefully derived variational lower bound to approximate the marginal log-likelihood function, resulting in closed-form estimates. This method significantly improves the computational efficiency and is viable for high-dimensional applications. Additionally, an importance-weighted GVEM (IW-GVEM) algorithm is developed for MGRM to address the bias issue. Simulation studies show that our GVEM and IW-GVEM run significantly faster than the MH-RM algorithm and are of competitiveness in both confirmatory and exploratory analysis. Our proposed algorithms are illustrated by analyzing a real dataset from the Big-Five Personality test.

报告人简介：徐平峰，男，吉林辽源人。东北师范大学前沿交叉研究院研究员，博士生导师。2010年毕业于东北师范大学概率论与数理统计专业。先后访问香港浸会大学、美国威斯康星大学麦迪逊校区、香港恒生管理学院、香港恒生大学等多次。主要从事复杂网络分析、图模型、因果推断、教育统计与心理测量、等研究方向，在JCGS、STAT&COMP、CSDA、BJMSP、Psychometrika等期刊发表SCI/SSCI论文多篇。主持国家自然科学基金青年基金1项、面上项目1项，主持国家社会科学基金一般项目1项。获吉林省科学技术奖自然科学三等奖1项。

报告时间：2024年9月10日上午10：00-11：00

报告地点：腾讯会议： 832930202

联系人：胡涛