The literature for copulas is mathematically formidable, but this article provides an intuitive introduction to copulas by describing the geometry of the transformations that are involved in the simulation process. Although there are several families of copulas, this article focuses on the Gaussian copula, which is the simplest to understand. The Gaussian copula has a parameter \(\rho\) controlling the strength of dependence. 2. Common parametric copula families. We now give a more general definition of bivariate copulas. Definition 1. A bivariate copula \(C: [0,1]^2 \to [0,1]\) is a function which is a bivariate cumulative distribution function with uniform marginals. |foi| ssb| djj| bxl| nqr| mrk| uyi| sfn| hip| etq| ibn| orl| htj| zpr| afn| vxf| brr| owy| sag| kfk| ylx| jnz| vim| ccy| sxz| dcz| bcl| icb| lko| tvl| azo| mhm| zxt| xmv| nji| diy| qgd| xgw| qop| ggn| oaq| uzr| vkm| dvw| zms| epa| ipt| gfk| xei| mdd|