A new approach to weighting and inference in sample surveys
抽样调查加权与推断的新方法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：摘要研究一般抽样调查权重与总体总量估计，未提供因果、面板、分布效应或其他所列细分方向的明确依据。
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…
关注这篇论文的作者
抽样调查加权与推断的新方法
- 研究问题
- 当设计权重高度分散且与目标变量关系较弱时，如何提高总体总量估计效率，同时为多用途调查提供统一的权重？
- 方法与思路
- 作者通过适当模型平滑设计权重或校准权重，重点改进 Horvitz–Thompson 估计量；推断只对权重建模，并讨论向校准估计的扩展。
- 材料陈述的贡献
- 摘要称提出了具有改善效率潜力的估计方法，给出理论论证与模拟评价，并以同一组平滑权重服务多个调查变量。
- 阅读关注点
- 应核对权重模型的设定与拟合、平滑后的偏差和方差、推断有效性所需条件，以及相对原设计估计量的效率比较。
- 阅读边界
- 仅据所给 OpenAlex 摘要，不能判断效率改善是否普遍成立；模型误设后果、具体理论条件和模拟数值均未说明。
原文摘要
The validity of design-based inference is not dependent on any model assumption. However, it is well known that estimators derived through design-based theory may be inefficient for the estimation of population totals when the design weights are weakly related to the variables of interest and have widely dispersed values. We propose estimators that have the potential to improve the efficiency of any estimator derived under the design-based theory. Our main focus is limited to the improvement of the Horvitz--Thompson estimator, but we also discuss the extension to calibration estimators. The new estimators are obtained by smoothing design or calibration weights using an appropriate model. Our approach to inference requires the modelling of only one variable, the weight, and it leads to a single set of smoothed weights in multipurpose surveys. This is to be contrasted with other model-based approaches, such as the prediction approach, in which it is necessary to postulate and validate a model for each variable of interest leading potentially to variable-specific sets of weights. Our proposed approach is first justified theoretically and then evaluated through a simulation study. Copyright 2008, Oxford University Press.