# A class of smooth, possibly data-adaptive nonparametric copula estimators containing the empirical beta copula

@inproceedings{Kojadinovic2021ACO, title={A class of smooth, possibly data-adaptive nonparametric copula estimators containing the empirical beta copula}, author={Ivan Kojadinovic and Bingqing Yi}, year={2021} }

A broad class of smooth, possibly data-adaptive nonparametric copula estimators that contains empirical Bernstein copulas introduced by Sancetta and Satchell (and thus the empirical beta copula proposed by Segers, Sibuya and Tsukahara) is studied. Within this class, a subclass of estimators that depend on a scalar parameter determining the amount of marginal smoothing and a functional parameter controlling the shape of the smoothing region is speciﬁcally considered. Empirical investigations of…

## 2 Citations

### Resampling techniques for a class of smooth, possibly data-adaptive empirical copulas

- Mathematics, Computer Science
- 2023

This work investigates the validity of two resampling techniques when carrying out inference on the underlying unknown copula using a recently proposed class of smooth, possibly data-adaptive nonparametric estimators that contains empirical Bernstein copulas (and thus the empirical beta copula).

### Testing Symmetry for Bivariate Copulas using Bernstein Polynomials

- Mathematics, Computer Science
- 2022

Simulation study and real data application showed that the Bernstein tests outperform the tests based on the empirical copula.

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