Estimators Based on Several Stratified Samples with Applications to Multiple Frame Surveys
基于多个分层样本的估计及多抽样框调查应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究多抽样框调查与分层抽样估计，未明确对应给定经济计量细分领域。
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基于多个分层样本的估计及多抽样框调查应用
- 研究问题
- 多个抽样框分别独立抽取分层简单随机样本且能识别重复单位时，如何整合估计，并简化重叠域和方差处理？
- 方法与思路
- 作者将多框样本视为同一框的多个独立样本，应用 Horvitz–Thompson、比率及迭代比例估计，并讨论数值线性化。
- 材料陈述的贡献
- 摘要称，调查实例中所提估计量在某些情形方差较低、计算较简单且便于扩展至多框，并提出方差计算的简化思路。
- 阅读关注点
- 应核对重复单位识别与独立抽样假设、重叠域权重、与 Hartley 估计量的比较条件，以及数值线性化的实施方式。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；较低方差仅见于所述部分情形，更复杂设计的保证及后续版本变化未说明。
原文摘要
In this article, a new method for producing estimates from a multiple frame survey is presented. The discussion in this article is restricted to sample designs in which a stratified simple random sample is selected independently from each frame. The estimation technique outlined, however, can be applied to more complex sample designs. It is assumed that units selected in more than one sample can be identified. This estimation technique is based on the fact that a multiple frame sample can be viewed as a special case of selecting two or more samples independently from the same frame. As a result, standard techniques from the literature for estimating from a single frame, such as the Horvitz—Thompson estimator or ratio estimation, can be applied to multiple frame samples. These standard techniques for estimating from a single frame are compared, based on data from a Statistics Canada survey, with the usual estimators suggested in the literature for estimating from a multiple frame sample design. These multiple frame estimators were first proposed by Hartley (1962). They have a common feature of averaging together separate estimates from two or more frames of the domains corresponding to the overlap of these frames. The numerical example given shows that the estimators suggested in this article provide lower variances in certain cases than the Hartley estimators. These estimators are also simpler to compute and extend more easily to three or more frames than the Hartley estimators. One of the single frame estimators considered is the raking ratio estimator. The complexity of its variance formula has discouraged its use in the past. In the numerical example, it is suggested that this problem can be circumvented by the simple technique of numerically linearizing the values of the observations repeatedly.