Endogenous post-stratification in surveys: Classifying with a sample-fitted model
调查中的内生事后分层：使用样本拟合模型分类AI 解读 ↓
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调查中的内生事后分层：使用样本拟合模型分类
- 研究问题
- 当调查样本既用于拟合分类模型又用于事后分层估计时，形成的随机分层会怎样影响一致性和渐近方差？
- 方法与思路
- 作者用样本拟合的广义线性模型生成预测值，按预定区间形成分层，并在抽样设计与超总体框架下研究估计量。
- 材料陈述的贡献
- 摘要称，建立设计一致性及超总体下的渐近正态性，渐近方差与固定分层相同，模拟中的模型拟合影响较小。
- 阅读关注点
- 应核对抽样设计、广义线性模型条件、区间划分和总体分层计数的处理，并查看两类一致性结论的差别。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；所谓温和条件和模拟设置未列出，不能推广到任意分类模型或声称有限样本等价。
原文摘要
Post-stratification is frequently used to improve the precision of survey estimators when categorical auxiliary information is available from sources outside the survey. In natural resource surveys, such information is often obtained from remote sensing data, classified into categories and displayed as pixel-based maps. These maps may be constructed based on classification models fitted to the sample data. Post-stratification of the sample data based on categories derived from the sample data (“endogenous post-stratification”) violates the standard post-stratification assumptions that observations are classified without error into post-strata, and post-stratum population counts are known. Properties of the endogenous post-stratification estimator are derived for the case of a sample-fitted generalized linear model, from which the post-strata are constructed by dividing the range of the model predictions into predetermined intervals. Design consistency of the endogenous post-stratification estimator is established under mild conditions. Under a superpopulation model, consistency and asymptotic normality of the endogenous post-stratification estimator are established, showing that it has the same asymptotic variance as the traditional post-stratified estimator with fixed strata. Simulation experiments demonstrate that the practical effect of first fitting a model to the survey data before post-stratifying is small, even for relatively small sample sizes.