Outlier Robust Finite Population Estimation
Outlier Robust Finite Population Estimation
复制标题
异常值稳健有限总体估计
DOI：
10.1080/01621459.1986.10478374
复制
发表时间：
1986
影响因子：
3.7
通讯作者：
R. Chambers
中科院分区：
文献类型：
--
作者：
R. Chambers
摘要样本数据中的异常值是应用调查统计学家长期面临的问题。此外，对于这个问题，传统的抽样调查理论没有提供真实的解决办法，只是提出了这样一个明智的建议，即在估计时不应对这些抽样要素进行最大程度的加权。样本离群值可以被识别为两种基本类型。这里我们关注的是第一种类型，可以方便地称为代表性离群值。这些是具有已正确记录的值的示例元素，并且不能假定其是唯一的。也就是说，没有充分的理由假设在目标总体的非抽样部分中没有更多类似的离群值。其余的样本离群值，默认情况下称为非代表性，是其数据值不正确或在某种意义上唯一的样本元素。处理这些不具代表性的离群值的方法基本上属于调查编辑和插补理论的范围，因此，不考虑在…
Abstract Outliers in sample data are a perennial problem for applied survey statisticians. Moreover, it is a problem for which traditional sample survey theory offers no real solution, beyond the sensible advice that such sample elements should not be weighted to their fullest extent in estimation. Sample outliers can be identified as of two basic types. Here we are concerned with the first type, which may conveniently be termed representative outliers. These are sample elements with values that have been correctly recorded and that cannot be assumed to be unique. That is, there is no good reason to assume there are no more similar outliers in the nonsampled part of the target population. The remaining sample outliers, which by default are termed nonrepresentative, are sample elements whose data values are incorrect or unique in some sense. Methods for dealing with these nonrepresentative outliers lie basically within the scope of survey editing and imputation theory and are, therefore, not considered in ...