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Estimates for geographical domains through geoadditive models in presence of incomplete geographical information

机译：在地理信息不完整的情况下，通过地理加和模型对地理域进行估算

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The paper deals with the matter of producing geographical domains estimates for a variable with a spatial pattern in presence of incomplete information about the population units location. The spatial distribution of the study variable and its eventual relations with other covariates are modeled by a geoadditive regression. The use of such a model to produce model-based estimates for some geographical domains requires all the population units to be referenced at point locations, however typically the spatial coordinates are known only for the sampled units. An approach to treat the lack of geographical information for non-sampled units is suggested: it is proposed to impose a distribution on the spatial locations inside each domain. This is realized through a hierarchical Bayesian formulation of the geoadditive model in which a prior distribution on the spatial coordinates is defined. The performance of the proposed imputation approach is evaluated through various Markov Chain Monte Carlo experiments implemented under different scenarios.
机译：本文涉及在存在有关人口单位位置的不完整信息的情况下针对具有空间模式的变量生成地理域估计的问题。研究变量的空间分布及其与其他协变量的最终关系通过地理叠加回归建模。使用这种模型来生成某些地理域的基于模型的估计值，需要在点位置处引用所有人口单位，但是通常，空间坐标仅对于采样单位是已知的。提出了一种针对非采样单位处理缺乏地理信息的方法：建议在每个域内的空间位置上施加分布。这是通过地理可加性模型的分级贝叶斯公式实现的，其中定义了空间坐标上的先验分布。通过在不同情况下实施的各种马尔可夫链蒙特卡洛实验评估了拟议的插补方法的性能。

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