强大的功能性考克斯回归模型
Gizel Bakicierler Sezer1, Ufuk Beyaztas2
1Department of Statistics, Marmara University, Kadikoy, 34722, Istanbul, Turkey. gizel.bakicierler@marmara.edu.tr.
Lifetime data analysis
|February 22, 2026
概括
本研究引入了一种强大的功能性考克斯回归模型,用于处理生存分析中的异常值. 新方法通过减轻异常数据点来提高准确性,优于现有技术.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 生存分析的分析.
背景情况:
- 具有功能共变量的经典考克斯比例危险模型对异常值敏感.
- 现有的功能性考克斯模型缺乏稳定性,影响时间到事件的结果评估.
研究的目的:
- 开发一个强大的功能性考克斯回归模型,耐异常值.
- 为了提高生存分析的可靠性,当功能数据包含异常观察时.
主要方法:
- 结合投影-追求强大的功能主要组件分析 (RPCA) 进行尺寸缩小.
- 在有限维子空间中使用强大的部分概率方法进行参数估计.
- 包含了强大的功能主要组件和标量共变量.
主要成果:
- 提出的强大的功能性考克斯模型与经典和处罚方法相比,表现优越,特别是与异常倾向的数据.
- 确定了包括一致性和正常性在内的非对称性属性.
- 影响函数分析证实了强度特征.
结论:
- 强大的功能性考克斯回归模型为存活分析提供了可靠的替代方案,功能数据包含异常值.
- 该方法在现实应用中是有效的,正如国家健康和营养检查调查加速度数据所示.
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