摘要翻译：
本文的目的是利用2019年英国举行的欧洲选举投票来重新审视2016年欧盟公投投票后进行的分析。随着英国在2020年退出欧盟，这一演习提供了一个公众舆论的舞台。采用看似无关的回归框架中的组成数据分析，尊重投票结果的组成性质；每个结果都是一个份额，加起来是100%，每个结果都与备选方案有关。每个计算领域的当代解释数据来自社会人口统计、就业、生活满意度和地点等主题。研究发现，在英国，根据年龄、资格、就业和地方，仍然存在强烈而明显的分歧。成分分析方法的使用在解释这些模型方面产生了挑战，但边缘图被认为在一定程度上有助于解释。
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英文标题：
《Who voted for a No Deal Brexit? A Composition Model of Great Britains
  2019 European Parliamentary Elections》
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作者：
Stephen Clark
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最新提交年份：
2020
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分类信息：

一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  The purpose of this paper is to use the votes cast at the 2019 European elections held in United Kingdom to re-visit the analysis conducted subsequent to its 2016 European Union referendum vote. This exercise provides a staging post on public opinion as the United Kingdom moves to leave the European Union during 2020. A composition data analysis in a seemingly unrelated regression framework is adopted that respects the compositional nature of the vote outcome; each outcome is a share that adds up to 100% and each outcome is related to the alternatives. Contemporary explanatory data for each counting area is sourced from the themes of socio-demographics, employment, life satisfaction and place. The study find that there are still strong and stark divisions in the United Kingdom, defined by age, qualifications, employment and place. The use of a compositional analysis approach produces challenges in regards to the interpretation of these models, but marginal plots are seen to aid the interpretation somewhat.
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PDF链接：
https://arxiv.org/pdf/2001.06548