论文标题
确定最合适的分类响应订单
Identifying the Most Appropriate Order for Categorical Responses
论文作者
论文摘要
分类反应自然会在各种科学学科中产生。在许多情况下，没有预定的响应类别顺序，并且必须将响应建模为名义。在这项研究中，我们将响应类别的顺序视为统计模型的一部分，并表明可以使用基于可能性的模型选择标准选择真实顺序。出于预测目的，即使不存在真实顺序，具有所选顺序的统计模型也可能胜过基于名义响应的模型。对于广泛用于分类响应的多项式逻辑模型，我们显示了基于可能性标准无法区分的理论上等效订单的存在，并确定其最大似然估计器之间的连接。我们使用仿真研究和真实数据分析来确认选择最合适的分类响应顺序的需求和好处。
Categorical responses arise naturally within various scientific disciplines. In many circumstances, there is no predetermined order for the response categories, and the response has to be modeled as nominal. In this study, we regard the order of response categories as part of the statistical model, and show that the true order, when it exists, can be selected using likelihood-based model selection criteria. For predictive purposes, a statistical model with a chosen order may outperform models based on nominal responses, even if a true order does not exist. For multinomial logistic models, widely used for categorical responses, we show the existence of theoretically equivalent orders that cannot be differentiated based on likelihood criteria, and determine the connections between their maximum likelihood estimators. We use simulation studies and a real-data analysis to confirm the need and benefits of choosing the most appropriate order for categorical responses.
