规范化联合最大概率估计潜空间项目响应模型
Dylan Molenaar1, Minjeong Jeon2
1Department of Psychology, University of Amsterdam, The Netherlands.
Psychometrika
|January 9, 2026
概括
我们为使用规范化联合最大概率 (JML) 的潜空间物体响应模型 (LSIRMs) 引入了更快的估计方法. 这些方法可以有效地分析复杂的项目响应数据,包括顺序结果.
科学领域:
- 心理测量 心理测量 心理测量
- 统计建模 统计建模
背景情况:
- 隐藏空间物体响应模型 (LSIRMs) 在低维的欧几里德空间中嵌入主体和物体,揭示了超出传统物体响应理论的相互作用.
- 目前的马尔科夫链蒙特卡洛 (MCMC) 对LSIRM的贝叶斯估计是计算密集的,限制了它们的实际应用.
研究的目的:
- 为LSIRM提出和评估规范化联合最大概率 (JML) 估计方法.
- 解决计算挑战,并将LSIRM的适用性扩展到顺序数据和维度选择.
主要方法:
- 开发了两种规范化JML估计的变体:处罚JML和约束JML.
- 为LSIRM估计推导出JML方法,解决最大概率的特定问题.
- 使用交叉验证来选择潜空间维度.
主要成果:
- 模拟研究表明可接受的参数恢复和有效的交叉验证性能.
- 将二进制和顺序LSIRM应用于关于演推理和人格的真实数据集.
- 在R包"LSMjml"中实施的JML方法.
结论:
- 规范化的JML估计为LSIRM提供了一个计算效率高的MCMC替代方案.
- 拟议的方法促进了LSIRM的更广泛应用,包括对顺序数据和自动化维度选择.
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