拉什模型
可解释性
多向拉希模型
混合模型
计算机科学
班级(哲学)
二进制数
潜在类模型
多元统计
潜变量
人工智能
机器学习
数学
统计
项目反应理论
心理测量学
算术
出处
期刊:Information
[MDPI AG]
日期:2022-11-10
卷期号:13 (11): 534-534
被引量:2
摘要
The mixture Rasch model is a popular mixture model for analyzing multivariate binary data. The drawback of this model is that the number of estimated parameters substantially increases with an increasing number of latent classes, which, in turn, hinders the interpretability of model parameters. This article proposes regularized estimation of the mixture Rasch model that imposes some sparsity structure on class-specific item difficulties. We illustrate the feasibility of the proposed modeling approach by means of one simulation study and two simulated case studies.
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