对于缺少响应变量和易出错的共变量等部分线性模型的最佳模型平均值
Zhongqi Liang1,2, Suojin Wang3, Li Cai4
1School of Data Sciences, Zhejiang University of Finance & Economics, Hangzhou, China.
Statistics in medicine
|July 26, 2024
概括
本研究引入了一种新的最佳模型平均方法,用于缺少数据和测量错误的部分线性模型. 拟议的方法实现最小平方损失,在模拟中超过现有技术.
科学领域:
- 统计 统计 统计 统计
- 计量经济学 计量经济学
- 生物统计学 生物统计学
背景情况:
- 部分线性模型在统计建模中被广泛使用.
- 缺失的响应和共变量的测量错误在数据分析中带来了重大挑战.
- 模型平均的目的是通过结合多个模型来改进估计.
研究的目的:
- 为部分线性模型开发一种最佳的模型平均方法,这些模型具有缺失的响应和错误的共变量.
- 为模型平均化提出一种新的权重选择标准.
- 为了确定拟议的估计器的非对称最佳性和收性质.
主要方法:
- 一个马洛斯类型的标准适用于模型平均的重量选择.
- 模型平均估计器的异面性质在规律性条件下得到.
- 权重向量的局部最小化和收率在理论上已经确立.
- 模拟研究是为了将拟议的方法与现有方法进行比较而进行的.
主要成果:
- 拟议的模型平均估计器被证明在最小化二次损失方面是异常最优的.
- 证明了局部最小化重量向量的存在和收率.
- 模拟结果表明,拟议方法的性能优于现有方法.
结论:
- 这种新型的模型平均化技术有效地解决了部分线性模型中缺少的数据和测量错误的问题.
- 该方法提供了一个具有可取的理论性质的异常最佳估计器.
- 该方法通过模拟进行验证,并在HIV-CD4数据集上进行证明.
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