Hierarchical Bayes small area estimation with an unknown link function.
Hierarchical Bayes small area estimation with an unknown link function.
复制标题
具有未知链接函数的分层贝叶斯小区域估计。
DOI：
10.1111/sjos.12376
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发表时间：
2019
影响因子：
1
通讯作者：
J. N. K.
中科院分区：
文献类型：
--
作者：
Sugasawa;S.;Kubokawa;T. and Rao;J. N. K.
区域水平的不匹配采样和连接模型已被广泛用作一种基于模型的方法，用于产生小区域均值的可靠估计。然而，一个实际的困难是链接函数的规范。本文通过不指定连杆函数的形式并从数据中估计连杆函数，放宽了对已知连杆函数的假设。采用惩罚样条法估计链接函数，采用马尔可夫链蒙特卡罗方法进行后验计算，提出了估计面积均值的分层贝叶斯方法。仿真研究结果表明，该方法与基于已知链接函数的传统方法进行了比较。此外，建议的方法也适用于日本家庭收入和支出调查的数据和西班牙各省的贫困率。
Area‐level unmatched sampling and linking models have been widely used as a model‐based method for producing reliable estimates of small‐area means. However, one practical difficulty is the specification of a link function. In this paper, we relax the assumption of a known link function by not specifying its form and estimating it from the data. A penalized‐spline method is adopted for estimating the link function, and a hierarchical Bayes method of estimating area means is developed using a Markov chain Monte Carlo method for posterior computations. Results of simulation studies comparing the proposed method with a conventional approach based on a known link function are presented. In addition, the proposed method is applied to data from the Survey of Family Income and Expenditure in Japan and poverty rates in Spanish provinces.