使用先前数据冲突调整贝叶斯规范回归模型.
Timofei Biziaev1, Karen Kopciuk1,2,3, Thierry Chekouo1,4
1Department of Mathematics and Statistics, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4 Canada.
Statistics and computing
|February 24, 2025
概括
本研究介绍了一种经验贝叶斯方法来配置贝叶斯调节回归模型. 该方法在高维设置中改善了变量选择,特别是当真实效应很小时.
科学领域:
- 统计 统计 统计 统计
- 计算生物学 计算生物学
- 生物统计学 生物统计学
背景情况:
- 高维回归模型为变量选择提出了计算和理论上的挑战.
- 贝叶斯规则化的回归与收缩先验 (例如,拉普拉斯,尖和板块) 提供有效的变量选择,当先验配置良好时.
研究的目的:
- 提出一个实证贝叶斯配置方法,用于贝叶斯规则化回归中的收缩先验.
- 评估这种方法在高维线性回归模型的变量选择中的性能.
主要方法:
- 开发了一种使用先前数据冲突检查的经验贝叶斯配置.
- 将该方法应用于贝叶斯式LASSO和spike-and-slab priors. 应用该方法对贝叶斯式LASSO和spike-and-slab priors进行了应用.
- 通过高维模拟和分析COVID-19蛋白质组数据来评估性能.
主要成果:
- 拟议的经验贝叶斯配置显示出潜在的超越竞争模型的性能,特别是当真正的回归效应很小时.
- 该方法成功地应用于模拟数据和真实世界的蛋白质组数据.
结论:
- 使用先前数据冲突检查的实证贝叶斯配置为贝叶斯规范回归中的变量选择提供了强大的方法.
- 这种方法提高了复杂数据集中的贝叶斯变量选择技术的可靠性和性能.
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