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Local stationarity in small area estimation models

机译：小区域估计模型中的局部平稳性

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摘要

Small area estimators are often based on linear mixed models under the assumption that relationships among variables are stationary across the area of interest (Fay-Herriot models). This hypothesis is patently violated when the population is divided into heterogeneous latent subgroups. In this paper we propose a local Fay-Herriot model assisted by a Simulated Annealing algorithm to identify the latent subgroups of small areas. The value minimized through the Simulated Annealing algorithm is the sum of the estimated mean squared error (MSE) of the small area estimates. The technique is employed for small area estimates of erosion on agricultural land within the Rathbun Lake Watershed (IA, USA). The results are promising and show that introducing local stationarity in a small area model may lead to useful improvements in the performance of the estimators.
机译：小区域估计量通常基于线性混合模型，其前提是变量之间的关系在感兴趣区域内是固定的（Fay-Herriot模型）。当总体分为不同的潜伏亚组时，显然违反了这一假设。在本文中，我们提出了一个模拟退火算法辅助的局部Fay-Herriot模型，以识别小区域的潜在子组。通过模拟退火算法最小化的值是小面积估算值的估算均方误差（MSE）之和。该技术可用于小面积估算Rathbun湖流域（美国，IA）内农业土地的侵蚀。结果令人鼓舞，表明在小区域模型中引入局部平稳性可能会导致估计器性能的有效提高。

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