在通用线性模型中用于组规范化和变量选择的多功能下降算法
1Department of Psychology, University of Minnesota.
概括
一个新的自适应边界梯度下降 (ABGD) 算法增强了组弹性净处罚回归. 这种灵活的框架在各种响应分布中提供了稳定的计算和广泛的适用性,提高了处罚回归模型的效率.
科学领域:
- 统计 统计 统计 统计
- 计算统计学 计算统计学
- 机器学习 机器学习
背景情况:
- 集团弹性净惩罚回归对于高维数据分析至关重要.
- 现有的算法往往缺乏灵活性和计算稳定性.
- 需要适用于各种响应分布和预测器集的方法.
研究的目的:
- 为组弹性净处罚回归提出一种新的自适应边界梯度下降 (ABGD) 算法.
- 为惩罚性回归开发一个灵活且计算稳定的框架.
- 将群体惩罚的适用性扩展到更广泛的响应分布范围.
主要方法:
- 开发一个自适应边界梯度下降 (ABGD) 算法.
- 为了计算稳定性,费舍尔信息矩阵的自适应边界.
- 在 `grpnet ` R 包中实现支持各种响应分布的实现 (高斯式,二项式,波桑式,多项式,负二项式,马式,反高斯式).
主要成果:
- 该ABGD算法提供了一个灵活和稳定的计算框架.
- 该方法不需要预测器正交并广泛适用.
- 模拟和真实数据表明,该算法是高效的,与常见分布的现有方法相匹配或超过,并使高维多项回归成为可能.
结论:
- 拟议的ABGD算法在惩罚回归中提供了显著的进步.
- 它的灵活性和效率使其适用于各种统计建模任务,包括高维基因组数据.
- 欧基网R包为研究人员提供了可访问的实施方案.
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