对于受限立方线回归的贪结选择算法
Jo Inge Arnes1, Alexander Hapfelmeier2, Alexander Horsch1
1Department of Computer Science, Faculty of Science and Technology, UiT The Arctic University of Norway, Tromsø, Norway.
限制立方线 (RCS) 回归结的选择可能会导致过拟合或低性能. 一个新的向后贪搜索算法改善了预测错误和模型匹配,为流行病学建模提供了更好的方法.
科学领域:
- 流行病学 流行病学
- 统计建模 统计建模
背景情况:
- 非线性回归,包括受限立方线回归 (RCS) 在流行病学中对于预测和估计变量关系至关重要.
- 使用量子的标准RCS节点放置可以导致密集数据区域的过拟合或稀疏区域的低性能.
研究的目的:
- 开发和评估一种新的节点选择方法,用于受限立方线回归.
- 解决流行病学模型中基于量子的标准结位的局限性.
主要方法:
- 一个贪的搜索算法,在RCS回归中使用一个向后选择方法来放置节点.
- 在一个名为"knutar"的开源R包中实现算法.
- 通过模拟实验,比较拟议的方法与标准的节点选择过程.
主要成果:
- 与标准方法相比,提出的向后贪搜索算法证明了预测错误的减少.
- 该算法还提高了贝叶斯信息标准的得分,表明模型更适合.
- 模拟实验证实了新的节点选择策略的有效性.
结论:
- 开发的贪的向后结选择算法为流行病学中RCS回归的标准方法提供了优越的替代方案.
- "knutar" R-package为研究人员提供了一种实用工具,可以实现这种改进的结节选择技术.
- 这种方法提高了采用非线性关系的流行病学模型的可靠性和通用性.
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