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A Bayesian cluster analysis of election results

机译:选举结果的贝叶斯聚类分析

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A Bayesian cluster analysis for the results of an election based on multinomial mixture models is proposed. The number of clusters is chosen based on the careful comparison of the results with predictive simulations from the models, and by checking whether models capture most of the spatial dependence in the results. By implementing the analysis on five recent elections in Barcelona, the reader is walked through the choice of the best statistics and graphical displays to help chose a model and present the results. Even though the models do not use any information about the location of the areas in which the results are broken into, in the example they uncover a four-cluster structure with a strong spatial dependence, that is very stable over time and relates to the demographic composition.
机译:提出了基于多项混合模型的选举结果的贝叶斯聚类分析。基于对结果与模型的预测模拟的仔细比较,并通过检查模型是否在结果中捕获了大部分空间相关性,来选择聚类的数量。通过对巴塞罗那最近的五次选举进行分析,引导读者选择最佳的统计数据和图形显示,以帮助选择模型并展示结果。即使模型不使用任何关于结果被划分为区域的位置的信息,在示例中,它们仍会发现具有强烈空间依赖性的四类结构,该结构随时间推移非常稳定,并且与人口统计有关组成。

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