一种可扩展的实证贝叶斯方法,用于在通用线性模型中对变量选择
Haim Y Bar1, James G Booth2, Martin T Wells2
1Department of Statistics University of Connecticut, Storrs CT, 06269, USA.
概括
一种新的经验贝叶斯方法增强了广义线性模型的变量选择. 这种计算效率高的算法有效地处理了许多解释变量,提高了模型的准确性.
科学领域:
- 统计 统计 统计 统计
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 变量选择对于构建准确的通用线性模型 (GLMs) 是至关重要的.
- 传统方法难以处理大量潜在的解释变量,超过了响应的数量.
- 现有的贝叶斯式方法可能是计算密集型,并缓慢地趋同.
研究的目的:
- 开发一种可扩展的经验贝叶斯方法,用于GLM中的变量选择.
- 为了应对高维解释变量的挑战,预测者可能远远超过观测.
- 创建一个计算效率高的算法,比基于模拟的方法更快地融合.
主要方法:
- 线性预测器中的系数的三组分混合模型.
- 模型系数作为随机效应 (正,负或无效).
- 使用通用交替最大化算法进行参数估计.
主要成果:
- 拟议的算法有效地扩展到非常多的解释变量.
- 估计参数的数量保持不变,无论预测因素的数量如何.
- 与基于模拟的完全贝叶斯方法相比,证明了明显更快的趋同.
结论:
- 新的经验贝叶斯方法为GLM中的变量选择提供了一个高效和可扩展的解决方案.
- 这种方法对于高维数据集特别有利.
- 该算法为现有的贝叶斯技术提供了一个计算上优越的替代方案.
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