Small area estimation with subgroup analysis
Small area estimation with subgroup analysis
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DOI：
10.1080/24754269.2019.1659097
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发表时间：
2019-07-03
影响因子：
0.5
通讯作者：
Zhu, Zhengyuan
中科院分区：
文献类型：
--
作者：
Wang, Xin;Zhu, Zhengyuan
针对小面积估计（SAE）问题，提出了一种基于成对惩罚回归方法的单元级模型。与传统模型中假设所有小域的回归系数相同不同的是，该估计器基于子群回归模型，允许在不同的组中使用不同的回归系数。采用乘数交替方向法（ADMM）算法寻找具有不同回归系数的子群。我们还考虑了空间面数据的两两空间权重。在仿真研究中，我们比较了新估计器与传统小面积估计器的性能。我们还使用来自爱荷华州国家资源清单调查的数据将新的估计器应用于城市面积估计。
In this article, a new unit level model based on a pairwise penalised regression approach is proposed for problems in small area estimation (SAE). Instead of assuming common regression coefficients for all small domains in the traditional model, the new estimator is based on a subgroup regression model which allows different regression coefficients in different groups. The alternating direction method of multipliers (ADMM) algorithm is used to find subgroups with different regression coefficients. We also consider pairwise spatial weights for spatial areal data. In the simulation study, we compare the performances of the new estimator with the traditional small area estimator. We also apply the new estimator to urban area estimation using data from the National Resources Inventory survey in Iowa.