# Solving estimating equations with copulas

@article{Nagler2018SolvingEE, title={Solving estimating equations with copulas}, author={Thomas Nagler and Thibault Vatter}, journal={arXiv: Methodology}, year={2018} }

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…

## 5 Citations

Copula-Based Regression Models With Data Missing at Random

- Mathematics
- 2020

The existing literature of copula-based regression assumes that complete data are available, but this assumption is violated in many real applications. The present paper allows the regressand and…

Explaining predictive models using Shapley values and non-parametric vine copulas

- Computer ScienceArXiv
- 2021

This paper proposes two new approaches for modelling the dependence between the features of Shapley values based on vine copulas, which are flexible tools for modelling multivariate non-Gaussian distributions able to characterise a wide range of complex dependencies.

D-vine copula based mean regression and a comparison with gradient boosting

- Computer Science
- 2018

This thesis uses the subclass of D-vine copulas in order to develop a new method for mean regression, which can achieve very good results in many different scenarios and can even outperform gradient boosting in some setups.

Stationary vine copula models for multivariate time series

- Computer ScienceJournal of Econometrics
- 2022

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