Bivariate nonparametric estimation of the Pickands dependence function using Bernstein copula with kernel regression approach

DOI10.1007/s00180-017-0750-2zbMath1417.62139OpenAlexW2738353250MaRDI QIDQ1695428

Alireza Ahmadabadi, Burcu Hudaverdi Ucer

Publication date: 7 February 2018

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00180-017-0750-2

zbMATH Keywords

convexitykernel regressionPickands dependence functionBernstein copulabivariate extreme value copula

Mathematics Subject Classification ID

Density estimation (62G07) Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05)

Related Items (6)

Central limit theorems of range-based estimators for diffusion models ⋮ Bivariate nonparametric estimation of the Pickands dependence function using Bernstein copula with kernel regression approach ⋮ On the weighted tests of independence based on Bernstein empirical copula ⋮ Unnamed Item ⋮ Bernstein Copulas and Composite Bernstein Copulas ⋮ Bayesian estimation of bivariate Pickands dependence function

Uses Software

• Qhull

Cites Work

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• Multivariate nonparametric estimation of the Pickands dependence function using Bernstein polynomials
• Qhull
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• Rank-based inference for bivariate extreme-value copulas
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• On the limiting behavior of the Pickands estimator for bivariate extreme- value distributions
• Distribution and dependence-function estimation for bivariate extreme-value distributions.
• Bivariate nonparametric estimation of the Pickands dependence function using Bernstein copula with kernel regression approach
• On uniform consistent estimators for convex regression
• Bivariate extreme value theory: Models and estimation
• A nonparametric estimation procedure for bivariate extreme value copulas
• The quickhull algorithm for convex hulls
• THE BERNSTEIN COPULA AND ITS APPLICATIONS TO MODELING AND APPROXIMATIONS OF MULTIVARIATE DISTRIBUTIONS
• Understanding Relationships Using Copulas

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