Downweighting Influential Clusters in Surveys
调查中高影响群组的降权处理AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要聚焦调查抽样中的稳健降权与偏差方差权衡，未直接对应所列领域细分。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
调查中高影响群组的降权处理
- 研究问题
- 当少数调查群组对估计及其方差影响过大时，如何预先规定客观的降权规则，并评估稳健处理引入的偏差？
- 方法与思路
- 作者对群组经验影响量应用 M 估计确定降权程度，在人口覆盖调查中用泰勒线性化估计影响，并采用 t 分布及 Huber 方法。
- 材料陈述的贡献
- 摘要称稳健处理在所用数据中比权重截断更能降低估计方差，且降低估计均方误差，同时指出影响分布不对称可能引入偏差。
- 阅读关注点
- 应核对群组影响量与降权规则的构造、调查设计如何进入估计，以及不对称情形下偏差和均方误差的评估方式。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；所述改善来自特定调查数据，摘要未给数值或普遍保证，不能外推。
原文摘要
Certain clusters may be extremely influential on survey estimates and consequently contribute disproportionately to their variance. We propose a general approach to estimation that downweights highly influential clusters, with the amount of downweighting based on M-estimation applied to the empirical influence of the clusters. The method is motivated by a problem in census coverage estimation, and we illustrate it by using data from the 1990 Post Enumeration Survey (PES). In this context, an objective, prespecified methodology for handling influential observations is essential to avoid having to justify judgmental post hoc adjustment of weights. In 1990, both extreme weights and large errors in the census led to extreme influence. We estimated influence by Taylor linearization of the survey estimator, and we applied M-estimators based on the t distribution and the Huber ψ-function. As predicted by theory, the robust procedures greatly reduced the estimated variance of estimated coverage rates, more so than did truncation of weights. On the other hand, the procedure may introduce bias into survey estimates when the distributions of the influence statistics are asymmetric. We consider the properties of the estimators in the presence of asymmetry, and we demonstrate techniques for assessing the bias-variance trade-off, finding that estimated mean squared error is reduced by applying the robust procedure to our dataset. We also suggest PES design improvements to reduce the impact of influential clusters.