Deletion Influence and Masking in Regression
回归中的删除影响与掩蔽AI 解读 ↓
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回归中的删除影响与掩蔽
- 研究问题
- 在线性回归中，逐个删除观测的Cook距离为何可能遗漏成组影响，联合删除与条件删除又如何呈现不同诊断现象？
- 方法与思路
- 作者针对已识别的观测对推导联合与条件Cook距离的一般形式，重点分析重复及相反解释变量配对，并用两个数据集说明。
- 材料陈述的贡献
- 摘要称，文章区分联合影响中的减弱、增强与淹没，以及条件影响中的掩蔽与提升，澄清这些现象的含义和使用范围。
- 阅读关注点
- 应核对联合与条件影响的定义、配对结构怎样改变诊断量，以及已识别观测对的分析能否支持实际异常观测筛查。
- 阅读边界
- 仅据所给Crossref摘要，不能判断诊断阈值或自动筛查效果；两个示例也不足以支持对全部回归数据的性能结论。
原文摘要
SUMMARY The paper is concerned with masking of Cook's distance, the popular deletion measure of influence of individual data cases in linear regression. Masking is broadly concerned with the limitations imposed by the use of individual cases. Two specific views are drawn from quotations: the first is associated with the established idea of joint influence, i.e. the deletion of two or more cases simultaneously, and the second is conditional influence, i.e. the calculation of Cook's distance before and after the deletion of other cases. Attention is mainly focused on pairs of already identified cases. Intuitive understanding is obtained via explicit general forms for joint and conditional Cook's distance influence with pairs of cases; especially tractable results are obtained for replicate and opposite explanatory pairs, and for multiples of individual cases of opposite pairs. The noteworthy effects possible in joint influence are described as reducing, enhancing and swamping; in conditional influence, interesting effects are described as masking and boosting. Thus only in connection with one effect in conditional influence is the term masking used. The work is illustrated on one constructed and one reported data set.