Spatial Misalignment Models for Small Area Estimation: A Simulation Study
Spatial Misalignment Models for Small Area Estimation: A Simulation Study
复制标题
小区域估计的空间失准模型：模拟研究
DOI：
10.1007/978-3-642-35588-2_25
复制
发表时间：
2013
期刊：
影响因子：
7.7
通讯作者：
A. Gelfand
中科院分区：
文献类型：
--
作者：
M. Trevisani;A. Gelfand
我们提出了一类未对齐的数据模型，用于解决典型的小面积估计（SAE）问题。特别是，我们扩展层次贝叶斯原子为基础的模型空间错位的SAE上下文，使辅助协变量的使用，这是可用的面积分区非嵌套的小面积的利益，沿着与计划域调查估计也不对齐与这些小面积。我们在原子水平上将潜在的感兴趣的特征建模为泊松变量，平均值作为种群大小和发病率的乘积。使用CAR模型或工艺规范引入空间随机效应。对于后者，入射率是整个区域上的空间点图案的高斯过程模型的函数。原子计数是通过积分原子上的点过程来驱动的。在所提出的模型类基准大面积估计自动满足。模拟研究探讨了所提出的模型，以改善传统的SAE模型估计的能力。
We propose a class of misaligned data models for addressing typical small area estimation (SAE) problems. In particular, we extend hierarchical Bayesian atom-based models for spatial misalignment to the SAE context enabling use of auxiliary covariates, which are available on areal partitions non-nested with the small areas of interest, along with planned domains survey estimates also misaligned with these small areas. We model the latent characteristic of interest at atom level as a Poisson variate with mean arising as a product of population size and incidence. Spatial random effects are introduced using either a CAR model or a process specification. For the latter, incidence is a function of a Gaussian process model for the spatial point pattern over the entire region. Atom counts are driven by integrating the point process over atoms. In the proposed class of models benchmarking to large area estimates is automatically satisfied. A simulation study examines the capability of the proposed models to improve on traditional SAE model estimates.