Understanding how a society views certain policies, politicians, and events can help shape public policy, legislation, and even a political candidate's campaign. This paper focuses on using aggregated, or interval censored, polling data to estimate the times when the public opinion shifts on the US president's job approval. The approval rate is modelled as a Poisson segmented (joinpoint) regression with the EM algorithm used to estimate the model parameters. Inference on the change points is carried out using BIC based model averaging. This approach can capture the uncertainty in both the number and location of change points. The model is applied to president Trump's job approval rating during 2020. Three primary change points are discovered and related to significant events and statements.

中文翻译：

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改变总统批准：检测和理解间隔审查民意调查数据中的变化点

了解一个社会如何看待某些政策、政治家和事件可以帮助塑造公共政策、立法，甚至是政治候选人的竞选活动。本文侧重于使用聚合或间隔删失的民意调查数据来估计公众舆论在美国总统的工作批准上发生变化的时间。批准率被建模为泊松分段（连接点）回归，其中 EM 算法用于估计模型参数。使用基于 BIC 的模型平均对变化点进行推断。这种方法可以捕捉到变化点的数量和位置的不确定性。该模型应用于特朗普总统在 2020 年的工作支持率。三个主要变化点被发现并与重大事件和声明相关。