Seminar| Institute of Mathematical Sciences
Time: Thursday, January 22th, 2026,10:00-11:00
Location: IMS, RS408
Speaker: Xiangbin Meng, Northeast Normal University
Abstract: The assumption of a normal distribution for latent traits is a common practice in item response theory (IRT) models. Numerous studies have demonstrated that this assumption is often inadequate, impacting the accuracy of statistical inferences in IRT models. To mitigate this issue, Gaussian mixture modeling (GMM) for latent traits, known as GMM-IRT, has been proposed. Moreover, the GMM-IRT models can also serve as powerful tools for exploring the heterogeneity of latent traits. However, the computation of GMM-IRT model estimation encounters several challenges, impeding its widespread application. The purpose of this paper is to propose a reliable and robust computing method for GMM-IRT model estimation. Specifically, we develop a mixed stochastic approximation EM (MSAEM) algorithm for estimating the three--parameter normal ogive model with GMM for latent traits (GMM-3PNO). Crucially, the GMM-3PNO is augmented to be a complete data model within the exponential family, thereby substantially streamlining the computation of the MSAEM algorithm. Furthermore, the MSAEM algorithm adeptly avoid the label-switching issue, ensuring its convergence. Finally, simulation and empirical studies are conducted to validate the performance of the MSAEM algorithm and demonstrate the superiority of the GMM-IRT models.
About the speaker：孟祥斌，教授、任职于东北师范大学数学与统计学院统计系。目前主要关注潜变量测量模型参数估计的统计计算、作答过程数据的统计建模、异质性项目反应理论模型的统计推断等问题。在PSYCHOMETRIKA, MULTIVAR BEHAV RES、BRIT J MATH STAT PSY、J EDUC BEHAV STAT、J EDUC MEAS、《心理学报》等SSCI、SCI和CSSCI期刊发表论文10余篇。主持国家社科基金一般项目、国家自然科学基金面上项目和青年项目、吉林省自然科学基金面上项目和优秀青年人才基金项目等多项国家级和省部级项目。