可靠的崩分析:比较实证贝叶斯前后分析与混合模型的偏差和错误率
Matthew A Albrecht1, Razi Hasan1
1Western Australian Centre for Road Safety Research, School of Psychological Science, The University of Western Australia Perth Western Australia Australia.
Accident; analysis and prevention
|January 25, 2025
概括
负二项式通用线性混合模型 (NB-GLMM) 为估计道路安全干预效应提供了一种比传统实证贝叶斯方法更简单,更准确的方法. 这种方法有效地解决了诸如回归到平均值,流量量和时间趋势等混因素.
科学领域:
- 道路安全研究 道路安全研究
- 运输工程 运输工程 运输工程
- 统计建模 统计建模
背景情况:
- 估计道路安全干预措施的因果关系是复杂的.
- 关键的挑战包括回归到平均值,流量量和时间趋势.
- 当前的实证贝叶斯 (EB) 方法复杂,并不总是最佳的.
研究的目的:
- 为了比较负二项式通用线性混合模型 (NB-GLMM) 与分析道路安全干预措施的各种EB方法的性能.
- 在理想和偏差的选址条件下评估这些方法.
主要方法:
- 使用两个场景进行了模拟实验:随机控制设计和地点选择偏差.
- 数据被模拟在不同的治疗效果,过度分散和样本大小.
- 将标准EB方法与NB-GLMM进行比较,其中包含治疗和时间之间的相互作用术语.
主要成果:
- 在大多数场景中,NB-GLMM表现出卓越的性能,保持了I型错误率,并提供了较少偏差的估计.
- 大多数标准EB方法过于自由或有偏见,除了具有不同分散参数的EB方法.
- 混合效应建模改善了EB程序中的偏差.
结论:
- 带有相互作用项的标准NB-GLMM是一个足够的,不那么复杂的替代方案,以定制EB解决方案进行碰撞分析.
- 包括NB-GLMM在内的混合效应方法在各种条件下在偏差和错误率方面优于标准EB方法.
- 选择的分析方法应该是最不偏的,在理想条件下和选择偏差条件下都有边际错误率.
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