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Small area estimation methods under cut-off sampling

机译：截止采样下的小区估计方法

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摘要

Cut-off sampling is applied when there is a subset of units from the population from which getting the required information is too expensive or difficult and, therefore, those units are deliberately excluded from sample selection. If those excluded units are different from the sampled ones in the characteristics of interest, naive estimators may be severely biased. Calibration estimators have been proposed to reduce the design-bias. However, when estimating in small domains, they can be inefficient even in the absence of cut-off sampling. Model-based small area estimation methods may prove useful for reducing the bias due to cut-off sampling if the assumed model holds for the whole population. At the same time, for small domains, these methods provide more efficient estimators than calibration methods. Since model-based properties are obtained assuming that the model holds but no model is exactly true, here we analyze the design properties of calibration and modelbased procedures for estimation of small domain characteristics under cut-off sampling. Our results confirm that model-based estimators reduce the bias due to cut-off sampling and perform significantly better in terms of design mean squared error.
机译：当从中获得所需信息的人口的单位子集太昂贵或困难时，应用了截止采样，因此，这些单位被刻意排除在样本选择之外。如果那些被排除的单位与感兴趣的特征中的采样单位不同，那么Naive估计器可能会严重偏见。已经提出了校准估计器以减少设计 - 偏差。然而，在估计小​​型域时，即使在没有切断采样的情况下，它们也可以效率低下。基于模型的小面积估计方法可以证明如果假设的模型为整个人群保持截止采样，则可用于减少由于截止采样而导致的偏差。同时，对于小型域，这些方法提供比校准方法更有效的估计。自假设模型保持但没有模型的基于模型的属性，因此我们在此处分析了校准和型号的设计属性，以便在截止采样下估计小型域特征。我们的结果证实，基于型号的估计因截止采样而减少了偏差，并且在设计均方误差方面显着更好地执行。

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