Solving estimating equations with copulas

01/31/2018

∙

by   Thomas Nagler, et al.

∙

0

∙

share

Thanks to their aptitude to capture complex dependence structures, copulas are frequently used to glue random variables into a joint model with arbitrary one-dimensional margins. More recently, they have been applied to solve statistical learning problems such as regressions or classification. Framing such approaches as solutions of estimating equations, we generalize them in a unified framework. We derive consistency, asymptotic normality, and validity of the bootstrap for copula-based Z-estimators. We further illustrate the versatility such estimators through theoretical and simulated examples.