论文标题
COVID-19的最高密度区域的非参数估计
Nonparametric estimation of highest density regions for COVID-19
论文作者
论文摘要
最高密度区域是指包含相对较高密度点的水平集。它们从基础密度产生的随机样品中的估计可以确定相应分布的簇。可以考虑不同的非参数观点来完成此任务。从实际的角度来看，重建最高密度区域可以解释为确定热点的一种方式，这是理解Covid-19时空演变的关键任务。在这项工作中，我们通过广泛的模拟研究比较了经典插件方法的行为和最近提出的最高密度区域估计的混合算法。两种方法都应用于分析有关美国19例案例的真实数据集。
Highest density regions refer to level sets containing points of relatively high density. Their estimation from a random sample, generated from the underlying density, allows to determine the clusters of the corresponding distribution. This task can be accomplished considering different nonparametric perspectives. From a practical point of view, reconstructing highest density regions can be interpreted as a way of determining hot-spots, a crucial task for understanding COVID-19 space-time evolution. In this work, we compare the behavior of classical plug-in methods and a recently proposed hybrid algorithm for highest density regions estimation through an extensive simulation study. Both methodologies are applied to analyze a real data set about COVID-19 cases in the United States.
