Estimation of copulas via Maximum Mean Discrepancy

Pierre Alquier, Badr-Eddine Chérief-Abdellatif, Alexis Derumigny, Jean-David Fermanian

Research output: Working paper

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Abstract

This paper deals with robust inference for parametric copula models. Estimation using Canonical Maximum Likelihood might be unstable, especially in the presence of outliers. We propose to use a procedure based on the Maximum Mean Discrepancy (MMD) principle. We derive non-asymptotic oracle inequalities, consistency and asymptotic normality of this new estimator. In particular, the oracle inequality holds without any assumption on the copula family, and can be applied in the presence of outliers or under misspecification. Moreover, in our MMD framework, the statistical inference of copula models for which there exists no density with respect to the Lebesgue measure on $[0,1]^d$, as the Marshall-Olkin copula, becomes feasible. A simulation study shows the robustness of our new procedures, especially compared to pseudo-maximum likelihood estimation. An R package implementing the MMD estimator for copula models is available.
Original languageEnglish
Publication statusUnpublished - 1 Oct 2020

Keywords

  • stat.ME
  • math.ST
  • stat.CO
  • stat.TH

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