对连续和二进制数据的概括贝叶斯结构方程模型的评估
Konstantinos Vamvourellis1, Konstantinos Kalogeropoulos1, Irini Moustaki1
1Department of Statistics, London School of Economics, London, UK.
本研究引入了一种评估贝叶斯结构方程模型 (BSEM) 的新方法,该方法侧重于样本外预测. 这种方法改进了现有的模型匹配和数据支持指标.
科学领域:
- 统计 统计 统计 统计
- 心理测量 心理测量 心理测量
- 计算统计学 计算统计学
背景情况:
- 目前的贝叶斯结构方程建模 (BSEM) 严重依赖后期预测p值,在评估模型合适性方面存在局限性.
- 使用信息先验的近似零方法,为明确设置参数为零提供了替代方案.
研究的目的:
- 为BSEM提出一种新的模型评估范式,以解决后期预测p值的缺陷.
- 引入监测样本外预测性能的工具,用于评估假设模型.
- 增强连续和二进制数据的BSEM,包括分类和非正常分布的数据.
主要方法:
- 使用近似零方法,对诸如因子负载之类的参数提供信息的先验.
- 实施评分规则和模型评估的交叉验证.
- 引入一个项目-个体随机效应来建模复杂的数据类型.
主要成果:
- 拟议的方法表明有效监测样本外预测性能.
- 模拟实验和真实数据分析 (Big-5人格,法格斯特罗姆测试) 验证了这一方法.
- 这些工具适用于连续和二进制数据模型.
结论:
- 新型范式为评估BSEM提供了一个强大的方法,补充了现有的指标.
- 该方法为确定假设模型的数据支持提供了指导方针.
- 增强的工具提高了BSEM对各种数据结构的灵活性和适用性.
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