模型空间先验对具有模型不确定性的统计推理的影响
Anupreet Porwal1, Adrian E Raftery1
1Department of Statistics, University of Washington, Seattle, WA 98195, USA.
概括
贝塔双项模型空间先验,考虑模型大小的先验概率,最好在贝叶斯模型平均 (BMA) 中处理模型不确定性. 这些先驱在各种统计任务和数据集中表现优于其他先驱.
科学领域:
- 统计 统计 统计 统计
- 计算统计学 计算统计学
背景情况:
- 贝叶斯模型平均 (BMA) 对于统计推理至关重要,需要仔细规范模型和参数空间先验.
- 模型不确定性是统计建模的一个重大挑战.
研究的目的:
- 为了比较贝叶斯模型中不同模型空间先验的性能,贝叶斯模型的平均值.
- 评估先前规范如何影响变量选择和线性回归中的统计任务.
主要方法:
- 评估了八个参考模型空间先验和三个自适应参数先验.
- 使用14个真实数据集进行了广泛的模拟研究.
- 在参数估计,间隔估计,推断和预测任务中评估性能.
主要成果:
- 基于模型大小的先前概率的β-双项模型空间先验,证明了优越的平均性能.
- 这些先验的表现优于统一模型空间先验.
- 最近提出的复杂性先验结果相对较差.
结论:
- 贝塔双项先验为BMA中指定模型空间先验提供了一种有效的方法,特别是用于线性回归中的变量选择.
- 模型空间前的选择显著影响统计推理和预测准确性.
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