Small area estimation with spatially varying natural exponential families
Small area estimation with spatially varying natural exponential families
复制标题
具有空间变化的自然指数族的小区域估计
DOI：
10.1080/00949655.2020.1714048
复制
发表时间：
2020
影响因子：
1.2
通讯作者：
K.
中科院分区：
文献类型：
--
作者：
Sugasawa;S.;Kawakubo;Y. and Ogasawara;K.
两阶段分层模型已广泛用于小面积估计，以产生面积平均值的间接估计。当区域被互换处理并且模型参数被假定为在所有区域上是相同的时，我们可能会在存在空间异质性的情况下失去效率。为了克服这个问题，我们考虑了一个两阶段的区域水平模型的基础上自然指数族的空间变化的模型参数。我们采用地理加权回归方法来估计变参数，并提出了一个新的面积平均值的经验贝叶斯估计。并讨论了相关问题，包括均方误差估计、基准估计和非抽样区域的估计。所提出的方法的性能进行评估，通过模拟和应用程序的两个数据集。
Two-stage hierarchical models have been widely used in small area estimation to produce indirect estimates of areal means. When the areas are treated exchangeably and the model parameters are assumed to be the same over all areas, we might lose the efficiency in the presence of spatial heterogeneity. To overcome this problem, we consider a two-stage area-level model based on natural exponential family with spatially varying model parameters. We employ geographically weighted regression approach to estimating the varying parameters and suggest a new empirical Bayes estimator of the areal mean. We also discuss some related problems, including the mean squared error estimation, benchmarked estimation and estimation in non-sampled areas. The performance of the proposed method is evaluated through simulations and applications to two data sets.