论文标题
基于极值理论的混合泊松模型的选择
Choice of mixture Poisson models based on Extreme value theory
论文作者
论文摘要
计数数据在许多应用领域中无所不在，由于零或极端值过多，通常会过度分散。借助代表优雅而有吸引力的建模策略的泊松分布的混合物，我们在这里着重于确定合适的混合分布的挑战性问题，并研究了如何使用极端价值理论。我们提出了一种原始策略，以在三个类别中选择最合适的候选人：fr {é} Chet，Gumbel和Pseudo-Gumbel。这种方法借助决策树，并通过数值模拟进行了评估。
Count data are omnipresent in many applied fields, often with overdispersion due to an excess of zeroes or extreme values. With mixtures of Poisson distributions representing an elegant and appealing modelling strategy, we focus here on the challenging problem of identifying a suitable mixing distribution and study how extreme value theory can be used. We propose an original strategy to select the most appropriate candidate among three categories: Fr{é}chet, Gumbel and pseudo-Gumbel. Such an approach is presented with the aid of a decision tree and evaluated with numerical simulations.
