在高维线性混合效应模型中使用EM算法来解决规范化问题
Daniela Cr Oliveira1, Fernanda L Schumacher2, Victor H Lachos3
1Department of Mathematics and Statistics, Federal University of Sao Joao del-Rei, Brazil.
新的EMLMLasso算法增强了线性混合效果模型的变量选择,特别是在高维设置中. 它在模拟和真实世界的数据中优于现有方法,提供了强大的和可通用的解决方案.
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:
- 预期最大化 (EM) 算法被广泛用于最大概率估计.
- 它在线性混合效应模型的高维规范化中的应用是有限的.
- 在这些复杂的统计模型中,有效的变量选择至关重要.
研究的目的:
- 介绍EMLMLasso算法用于高维线性混合效应模型中的变量选择.
- 评估EMLMLasso的性能与现有算法对比.
- 证明算法的稳定性和有效性,特别是当预测因素超过观察时.
主要方法:
- 将预期最大化 (EM) 算法与拉索规范化的R包glmnet结合起来.
- 实现自动调参数选择.
- 使用模拟和现实数据将EMLMLasso与glmmLasso和splmm进行比较.
主要成果:
- EMLMLasso展示了强大的和有效的变量选择能力.
- 算法表现良好,即使预测因素的数量 (p) 大于观察数量 (n).
- 在大多数评估的场景中,EMLMLasso的表现始终优于glmmLasso和splmm.
结论:
- 在高维线性混合效果模型中,EMLMLasso为变量选择提供了显著的进步.
- 该方法是一般的,简单的实施,并可扩展到其他处罚,如和弹性网.
- EMLMLasso为复杂的统计建模提供了对现有方法的优越替代方案.
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