A new approach to weighting and inference in sample surveys
A new approach to weighting and inference in sample surveys
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DOI：
10.1093/biomet/asn028
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发表时间：
2008-09-01
期刊：
影响因子：
2.7
通讯作者：
Beaumont, Jean-Francois
中科院分区：
文献类型：
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作者：
Beaumont, Jean-Francois
基于设计的推理的有效性不依赖于任何模型假设。然而，它是众所周知的，通过设计为基础的理论推导出的估计可能是低效的人口总数的估计时，设计权重是弱相关的变量的利益，并有广泛的分散值。我们提出的估计，有可能提高效率的任何估计的设计为基础的理论。我们的主要重点是有限的霍维茨-汤普森估计的改进，但我们也讨论了扩展到校准估计。通过使用适当的模型平滑设计或校准权重，获得新的估计量。我们的推理方法只需要一个变量的建模，重量，它会导致一个单一的一组平滑的权重在多用途调查。这是与其他基于模型的方法，如预测方法，它是必要的假设和验证模型的每个变量的利益，导致潜在的变量特定的权重集。我们提出的方法是第一次证明理论，然后通过模拟研究进行评估。
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.