论文标题
一种非参数贝叶斯项目响应建模方法，用于聚类项目和个人
A Nonparametric Bayesian Item Response Modeling Approach for Clustering Items and Individuals Simultaneously
论文作者
论文摘要
项目响应理论（IRT）是一种流行的建模范式，可根据测试或问卷调查中的离散响应来测量受试者特征和项目属性。关于项目和个人的异质性模式检测的讨论非常有限。在本文中，我们介绍了一种非参数贝叶斯方法，用于在Rasch模型下同时使用聚类和个人。具体而言，我们提出的方法基于有限混合物（MFM）模型的混合物。 MFM同时获得了项目和个人的聚类数量和聚类配置。通过模拟研究评估参数估计和参数聚类的性能，并应用了一个实际日期集来说明MFM Rasch建模。
Item response theory (IRT) is a popular modeling paradigm for measuring subject latent traits and item properties according to discrete responses in tests or questionnaires. There are very limited discussions on heterogeneity pattern detection for both items and individuals. In this paper, we introduce a nonparametric Bayesian approach for clustering items and individuals simultaneously under the Rasch model. Specifically, our proposed method is based on the mixture of finite mixtures (MFM) model. MFM obtains the number of clusters and the clustering configurations for both items and individuals simultaneously. The performance of parameters estimation and parameters clustering under the MFM Rasch model is evaluated by simulation studies, and a real date set is applied to illustrate the MFM Rasch modeling.
