在部分线性添加模型中修改了局部线性估计器,具有基于不同审查解决方案技术的右控数据
Ersin Yılmaz1, Dursun Aydın1, S Ejaz Ahmed2
1Department of Statistics, Mugla Sıtkı Kocman University, Mugla 48000, Turkey.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
本研究为右边审查的部分线性添加模型提供了一个修改后的局部线性估计器. 这种新方法提供了一个非代的解决方案,与卡普兰-梅尔权重和kNN归算表现良好.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 计量经济学 计量经济学
背景情况:
- 部分线性添加模型 (PLAM) 为统计建模提供了灵活的框架.
- 正确审查的数据在生存分析中很常见,需要专门的估计技术.
- 对于被审查的PLAM现有的方法可能是复杂的或范围有限的.
研究的目的:
- 为了引入一个修改的局部线性估计器 (LLR) 部分线性添加模型 (PLAM) 与右审查的响应变量.
- 为被审查的PLAM开发一种非代估计程序.
- 在PLAM中比较处理受审查数据的不同方法的性能.
主要方法:
- 使用修改的局部线性回归 (LLR) 方法.
- 采用修改后配算法进行非代估计.
- 研究了处理正确审查的三种方法:合成数据转换 (ST),卡普兰-梅尔权重 (KMW) 和kNN归算 (kNNI).
主要成果:
- 修改后的LLR为右边审查的PLAM提供了一个非代的解决方案.
- 使用ST和KMW的估计器的非对称性质得到了推导.
- 模拟研究和真实数据示例证明了LLR的有效性,特别是在KMW和kNNI.
结论:
- 拟议的修改局部线性估计器是一种可行和有效的方法,用于分析正确审查的部分线性添加模型.
- 在这种情况下,Kaplan-Meier权重和kNN归算是解决审查的有效策略.
- 在存在审查的情况下,LLR方法为现实世界的数据分析提供了实际优势.
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