In the Bayesian literature, a line of research called resolution of conflict is about the characterization of robustness against outliers of statistical models. The robustness characterization of a model is achieved by establishing the limiting behaviour of the posterior distribution under an asymptotic framework in which the outliers move away from the bulk of the data. The proofs of the robustness characterization results, especially the recent ones for regression models, are technical and not intuitive, limiting the accessibility and preventing the development of theory in that line of research. We highlight that the proof complexity is due to the generality of the assumptions on the prior distribution. To address the issue of accessibility, we present a significantly simpler proof for a linear regression model with a specific prior distribution corresponding to the one typically used. The proof is intuitive and uses classical results of probability theory. To promote the development of theory in resolution of conflict, we highlight which steps are only valid for linear regression and which ones are valid in greater generality. The generality of the assumption on the error distribution is also appealing; essentially, it can be any distribution with regularly varying or log-regularly varying tails. So far, there does not exist a result in such generality for models with regularly varying distributions. Finally, we analyse the necessity of the assumptions.

翻译：在贝叶斯文献中，一系列关于"冲突解析"的研究致力于刻画统计模型对异常值的稳健性。模型的稳健性特征通过建立后验分布在渐近框架下的极限行为来达成，该框架中异常值逐渐远离数据主体。稳健性特征结果的证明（特别是近期针对回归模型的证明）技术性强且缺乏直观性，限制了该研究领域的可及性并阻碍了理论发展。我们指出，证明的复杂性源于先验分布假设的普遍性。为提升可及性，我们针对采用典型先验分布的线性回归模型提出了显著简化的证明。该证明直观且运用了概率论的经典结果。为促进冲突解析领域的理论发展，我们明确了哪些步骤仅适用于线性回归，哪些步骤具有更广泛的适用性。误差分布假设的普适性同样具有吸引力：本质上可以是任何具有正则变化或对数正则变化尾部的分布。迄今为止，对于具有正则变化分布的模型，尚未存在如此普适性的结果。最后，我们对假设的必要性进行了分析。