Multivariate models and inference

超越多元正态性假设的多元模型被越来越多地使用。copula是构建多元模型的一种工具，在越来越多的科学领域得到了应用。所以这个领域的研究是基础科学研究，也是应用驱动的。Web of Science数据库显示，许多文章的标题中都有copula；具有许多copula应用的领域包括(i)商业、经济和金融，（ii）气候、环境和水文，（iii）工程和可靠性，（iv）农业、林业和生物统计学。对于更容易检测到偏离多元正态性的大型/庞大数据集，任何非高斯相关性的方法都将变得更加重要。

Multivariate models that go beyond the assumption of multivariate normality are increasingly being used. Copulas are one tool for constructing multivariate models, and copulas are used in more and more scientific areas. So this area of research is basic scientific research as well as application-motivated. The Web of Science database shows many articles with copula in the title; areas with many copula applications include (i) business, economics and finance, (ii) climate, environment and hydrology, (iii) engineering and reliability, (iv) agriculture, forestry and biostatistics. Any methodology for non-Gaussian dependence will becomes more important with large/huge data sets for which deviations from multivariate normality can be more easily detected.