In natural phenomena, data distributions often deviate from normality. One can think of cataclysms as a self-explanatory example: events that occur almost never, and at the same time are many standard deviations away from the common outcome. In many scientific contexts it is exactly these tail events that researchers are most interested in anticipating, so that adequate measures can be taken to prevent or attenuate a major impact on society. Despite such efforts, we have yet to provide definite answers to crucial issues in evaluating predictive solutions in domains such as weather, pollution, health. In this paper, we deal with two encapsulated problems simultaneously. First, assessing the performance of regression models when non-uniform preferences apply - not all values are equally relevant concerning the accuracy of their prediction, and there's a particular interest in the most extreme values. Second, assessing the robustness of models when dealing with uncertainty regarding the actual underlying distribution of values relevant for such problems. We show how different levels of relevance associated with target values may impact experimental conclusions, and demonstrate the practical utility of the proposed methods.
翻译:在自然现象中,数据分布往往偏离正常状态。人们可以将灾害分配视为一个不言自明的例子:几乎从未发生的事件,同时发生许多与共同结果不同的标准差。在许多科学环境中,研究人员最感兴趣的正是这些尾巴事件,正是这些尾巴事件,研究人员最感兴趣的是预测这些尾巴事件,以便采取充分的措施,防止或减轻对社会的重大影响。尽管作出了这些努力,但我们尚未对评估天气、污染、健康等领域的预测性解决办法的关键问题提供明确答案。在本文件中,我们同时处理两个包罗万象的问题。首先,在适用非统一偏好时评估回归模型的性能――并非所有数值都与其预测的准确性同等相关,而且对于最极端的值特别感兴趣。第二,评估模型在应对与这些问题相关的价值的实际基本分布的不确定性时的稳健性。我们要说明与目标值相关的不同程度可能对实验结论产生影响,并展示拟议方法的实际效用。