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A comprehensive framework of regression models for ordinal data

机译:序数数据回归模型的综合框架

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Literature on the models for ordinal variables grew very fast in the last decades and several proposals have been advanced when ordered data are expression of ratings, preferences, judgments, opinions, etc. A dichotomy has been emphasized between methods based on a latent variable which is behind the ordered selection and methods anchored to a probability distribution with a well defined pattern. In this paper, a comprehensive framework to regression models is proposed in case ordinal data come out from a discrete choice. The added value of this unifying perspective is the possibility to introduce further generalizations and also to deepen similarities and differences among the proposed models. A case study confirms the usefulness of this general framework. Some concluding remarks end the paper.
机译:在过去的几十年中,关于序数变量模型的文献增长很快,并且当有序数据表达等级,偏好,判断,观点等时,提出了一些建议。基于潜变量的方法之间的二分法被强调。在有序选择和方法的后面,锚定到具有明确定义模式的概率分布。在本文中,提出了一个综合的回归模型框架,以防序数数据来自离散选择。这种统一观点的附加价值是可以引入进一步的概括,并可以加深建议模型之间的异同。案例研究证实了此通用框架的有用性。一些结束语结束了本文。

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