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A practical approach for assessing the effect of grouping in hierarchical spatio-temporal models

机译：一种评估分层时空模型中分组效果的实用方法

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Hierarchical spatio-temporal models allow for the consideration and estimation of many sources of variability. A general spatio-temporal model can be written as the sum of a spatio-temporal trend and a spatio-temporal random effect. When spatial locations are considered to be homogeneous with respect to some exogenous features, the groups of locations may share a common spatial domain. Differences between groups can be highlighted both in the large-scale, spatio-temporal component and in the spatio-temporal dependence structure. When these differences are not included in the model specification, model performance and spatio-temporal predictions may be weak. This paper proposes a method for evaluating and comparing models that progressively include group differences. Hierarchical modeling under a Bayesian perspective is followed, allowing flexible models and the statistical assessment of results based on posterior predictive distributions. This procedure is applied to tropospheric ozone data in the Italian Emilia-Romagna region for 2001, where 30 monitoring sites are classified according to environmental laws into two groups by their relative position with respect to traffic emissions.
机译：分层的时空模型允许考虑和估计许多可变性来源。一般的时空模型可以写为时空趋势和时空随机效应之和。当空间位置相对于某些外源要素被认为是同质的时，位置组可以共享一个公共的空间域。群体之间的差异在大规模的时空成分和时空依赖结构中都可以得到强调。当这些差异未包括在模型规范中时，模型性能和时空预测可能会很弱。本文提出了一种评估和比较逐渐包含组差异的模型的方法。遵循贝叶斯视角下的分层建模，允许基于后验预测分布的灵活模型和结果统计评估。该程序适用于意大利艾米利亚-罗马涅地区2001年的对流层臭氧数据，该地区根据环境法根据交通排放的相对位置将30个监测点分为两类。

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