Outlier robust model‐assisted small area estimation
Outlier robust model‐assisted small area estimation
复制标题
异常值稳健模型辅助小区域估计
DOI：
10.1002/bimj.201200095
复制
发表时间：
2014
影响因子：
1.7
通讯作者：
N. Tzavidis
中科院分区：
文献类型：
--
作者：
E. Fabrizi;N. Salvati;M. Pratesi;N. Tzavidis
Chambers和Tzavidis提出了使用M分位数模型的小面积估计（）。这种方法的小面积估计的关键目标是获得可靠的和离群鲁棒的估计，同时避免了强参数假设的需要。然而，这种方法不允许使用单位水平的调查权重，使得估计量的设计一致性受到质疑，除非抽样设计在小区域内自加权。在本文中，我们采用了模型辅助的方法，并构造了基于M分位数小区域模型的设计相容小区域估计。分析和自助估计的设计为基础的方差进行了讨论。在复杂的抽样设计的存在下，建议的估计经验评估。
Small area estimation with M‐quantile models was proposed by Chambers and Tzavidis ( ). The key target of this approach to small area estimation is to obtain reliable and outlier robust estimates avoiding at the same time the need for strong parametric assumptions. This approach, however, does not allow for the use of unit level survey weights, making questionable the design consistency of the estimators unless the sampling design is self‐weighting within small areas. In this paper, we adopt a model‐assisted approach and construct design consistent small area estimators that are based on the M‐quantile small area model. Analytic and bootstrap estimators of the design‐based variance are discussed. The proposed estimators are empirically evaluated in the presence of complex sampling designs.