Nonparametric estimation of copulas and copula densities by orthogonal projections
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Abstract (arXiv)
In this paper we study nonparametric estimators of copulas and copula densities. We first focus our study on a density copula estimator based on a polynomial orthogonal projection of the joint density. A new copula estimator is then deduced. Its asymptotic properties are studied: we provide a large functional class for which this construction is optimal in the minimax and maxiset sense and we propose a method selection for the smoothing parameter. An intensive simulation study shows the very good performance of both copulas and copula densities estimators which we compare to a large panel of competitors. A real dataset in actuarial science illustrates this approach.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Oct 2020.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2024ROME: Robust Multi-Modal Density Estimatorcited by 2
- 2022Non-parametric Clustering of Multivariate Populations with Arbitrary Sizescited by 0
- 20232-Cats: 2D Copula Approximating Transformscited by 0
- 2024ROME: Robust Multi-Modal Density Estimator19 Jan 2024
- 20232-Cats: 2D Copula Approximating Transforms28 Sep 2023
- 2022Non-parametric Clustering of Multivariate Populations with Arbitrary Sizes11 Nov 2022