用许多相关和零膨胀预测器进行预测建模:评估非负的Garrote方法
Mariella Gregorich1, Michael Kammer1,2, Harald Mischak3,4
1Center for Medical Data Science, Institute of Clinical Biometrics, Medical University of Vienna, Vienna, Austria.
Statistics in medicine
|April 25, 2025
概括
我们开发了Ridge-garrote,这是一种用于质谱数据的新两阶段方法. 这种方法有效地减少了复杂的,零膨胀的特征,创造了精准度最小的损失的节预测模型.
科学领域:
- 生物统计学 生物统计学
- 计算生物学 计算生物学
- 蛋白质组学是指蛋白质组学.
背景情况:
- 质谱数据对预测建模提出了挑战,原因是相关的零膨胀特征.
- 资源密集型实验需要简洁和预测模型的特征减少.
研究的目的:
- 建立和检查零膨胀和相关预测者的规范化回归方法.
- 引入和评估一种新的两阶段规范化回归方法,Ridge-garrote.
主要方法:
- 开发了一种新的两阶段规则化回归方法 (ridge-garrote),使用和非负的garrotte估计器.
- 与一阶段 (ridge, lasso) 和其他两阶段 (lasso-ridge, ridge-lasso) 方法进行了比较.
- 通过模拟评估预测性表现和预测器选择,并使用皮体数据进行脏功能预测案例研究.
主要成果:
- 里奇-加罗特在模拟中始终选择比竞争对手更节的模型.
- 拉索提供了更高的预测准确性,但在选定的预测因素中具有很高的变化.
- 里奇拉索比里奇加罗特准确度略高,但选择了更多的噪音预测器.
结论:
- 里奇-加罗特为选择节的预测器集提供了实用的实用工具,在预测准确性方面做出了最小的妥协.
- 当变量选择不是优先事项时,Ridge是合适的.
- 方法的选择取决于预测准确性和模型节之间的平衡.
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