• Corpus ID: 247594356

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},
year={2021}
}
• Published 20 June 2021
• Mathematics, Computer Science
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

Figures from this paper

• 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).
• 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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