On multivariate Gaussian copulas

Gaussian copulas are handy tool in many applications. However, when dimension of data is large, there are too many parameters to estimate. Use of special variance structure can facilitate the task. In many cases, especially when different data types are used, Pearson correlation is not a suitable measure of dependence. We study the properties of Kendall and Spearman correlation coefficients—which have better properties and are invariant under monotone transformations—used at the place of Pearson coefficients. Spearman correlation coefficient appears to be more suitable for use in such complex applications.