贝叶斯的非参数隐性类分析与不同的项目类型
Meng Qiu1, Sally Paganin2, Ilsang Ohn3
1Department of Psychological Sciences, University of California, Merced.
Psychological methods
|March 20, 2025
概括
使用迪里克莱特过程混合物 (DPM) 的贝叶斯非参数隐性类分析 (LCA) 提供了一种灵活的方式来从数据中确定类的数量. 这种方法,DPM-MMLCA,有效地聚合了具有混合指标指标的个人.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 心理测量 心理测量 心理测量
背景情况:
- 隐性类分析 (LCA) 传统上需要预先指定类的数量,这往往会导致模型选择标准的模两可.
- 贝叶斯非参数方法,特别是迪里克莱特过程混合 (DPM),提供了一种数据驱动的方法来推断潜在类的数量.
研究的目的:
- 引入一种新的基于DPM的混合模式LCA模型 (DPM-MMLCA),用于使用混合度指标对个人进行集群.
- 开发和说明后置估计和类数和组成的推理程序的算法.
- 通过模拟,比较DPM-MMLCA与传统混合模式LCA的性能.
主要方法:
- 开发了一种基于迪里克莱特过程混合的混合模式隐性类分析 (DPM-MMLCA) 模型.
- 实现了两种用于后置估计的算法.
- 进行了模拟研究,评估了各种因素 (类数,变量数,样本大小,混合比例,类分离) 的性能.
主要成果:
- DPM-MMLCA模型有效地从数据中推断出潜在类的数量,克服了传统LCA的局限性.
- 模拟结果表明DPM-MMLCA在正确的类识别,参数恢复和标签分配方面的性能.
- 该方法通过三个现实数据示例和一个R/nimble教程来验证.
结论:
- 使用DPM的贝叶斯非参数LCA为混合模式数据分析提供了强大而灵活的替代方案.
- DPM-MMLCA为确定隐性类数量及其特征提供了实用解决方案.
- 该研究有助于在统计实践中实施先进的LCA技术.
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