Multivariate spatial count data are often segmented by unobserved space-varying factors that vary across space. In this setting, regression models that assume space-constant covariate effects could be too restrictive. Motivated by the analysis of cause-specific mortality data, we propose to estimate space-varying effects by exploiting a multivariate hidden Markov field. It models the data by a battery of Poisson regressions with spatially correlated regression coefficients, which are driven by an unobserved spatial multinomial process. It parsimoniously describes multivariate count data by means of a finite number of latent classes. Parameter estimation is carried out by composite likelihood methods, that we specifically develop for the proposed model. In a case study of cause-specific mortality data in Italy, the model was capable to capture the spatial variation of gender differences and age effects.

中文翻译：

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用于聚类多元空间计数数据的具有空间变化回归系数的模型

多变量空间计数数据通常由跨空间变化的未观察到的空间变化因素分割。在这种情况下，假设空间常数协变量效应的回归模型可能过于严格。受特定原因死亡率数据分析的启发，我们建议通过利用多元隐马尔可夫场来估计空间变化的影响。它通过一系列具有空间相关回归系数的泊松回归对数据进行建模，这些回归系数由未观察到的空间多项式过程驱动。它通过有限数量的潜在类来简洁地描述多变量计数数据。参数估计是通过复合似然方法进行的，我们专门为所提出的模型开发了这种方法。在意大利特定原因死亡率数据的案例研究中，