Annual Review of Statistics and Its Application ( IF 7.9 ) Pub Date : 2021-03-08 , DOI: 10.1146/annurev-statistics-040720-025210 Christophe Ley 1 , Slađana Babić 1, 2 , Domien Craens 1
Probability distributions are the building blocks of statistical modeling and inference. It is therefore of the utmost importance to know which distribution to use in what circumstances, as wrong choices will inevitably entail a biased analysis. In this article, we focus on circumstances involving complex data and describe the most popular flexible models for these settings. We focus on the following complex data: multivariate skew and heavy-tailed data, circular data, toroidal data, and cylindrical data. We illustrate the strength of flexible models on the basis of concrete examples and discuss major applications and challenges.
中文翻译:
带有应用程序的复杂数据的灵活模型
概率分布是统计建模和推断的基础。因此,最重要的是要知道在什么情况下使用哪种分布,因为错误的选择将不可避免地带来偏颇的分析。在本文中,我们将重点放在涉及复杂数据的环境上,并介绍针对这些设置的最受欢迎的灵活模型。我们关注以下复杂数据:多元偏斜和重尾数据,圆形数据,环形数据和圆柱数据。我们将在具体示例的基础上说明灵活模型的优势,并讨论主要的应用和挑战。