Models for Contingency Tables with Known Margins when Target and Sampled Populations Differ
目标总体与抽样总体不同时的已知边际列联表模型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：摘要聚焦列联表和抽样总体偏差，未明确涉及因果效应、面板或其他所列领域细分。
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…
关注这篇论文的作者
目标总体与抽样总体不同时的已知边际列联表模型
- 研究问题
- 当抽样框缺陷或抽样实施问题使抽样总体偏离目标总体时，已知边际的二维列联表应如何估计单元概率？
- 方法与思路
- 作者比较配比调整、随机抽样最大似然、最小卡方和最小二乘，将各方法对应到不同总体关联模型并推导标准误。
- 材料陈述的贡献
- 摘要称四种方法各有对应的最大似然模型；调查及模拟比较提示最小二乘较差，配比调整和随机抽样最大似然优于最小卡方。
- 阅读关注点
- 应核对各总体关联模型如何描述抽样偏差、已知边际如何进入估计，以及模拟生成机制和误差比较指标。
- 阅读边界
- 仅有版本未注明的 OpenAlex 索引摘要；所述方法比较依赖调查及模拟设置，不能据此推断普遍排序或刊本变化。
原文摘要
The analysis of two-way contingency tables with known margins is considered. Four methods for estimating the cell probabilities are compared, namely, raking (RAKE), maximum likelihood under random sampling (MLRS), minimum chi-squared (MCSQ), and least squares (LSQ). Assuming random sampling from the target population, these methods are known to be asymptotically equivalent, and small-sample studies have suggested that MCSQ is slightly better than the other methods in average root mean squared error. We consider properties of the methods when the sampled population differs from the target population, through deficiencies in the sampling frame or defects in the implementation of the sample. We show that each method is in fact maximum likelihood for a particular model relating the target and sampled populations. Expressions for the standard errors of the estimates are developed under these alternative models. The methods are compared on data from a health survey and in a simulation study where each of the methods is assessed using data generated in a variety of ways. The results suggest that LSQ is inferior to the other three methods, and RAKE and MLRS dominate MCSQ.