14 citations · 34 across the 16 of their papers we have counts for
20 papers
Estimation of time series by Maximum Mean Discrepancy
Pierre Alquier, Jean-David Fermanian, Benjamin Poignard
We define two minimum distance estimators for dependent data by minimizing some approximated Maximum Mean Discrepancy distances between the true empirical distribution of observati…
Distribution free MMD tests for model selection with estimated parameters
Florian Brück, Jean-David Fermanian, Aleksey Min
There exist some testing procedures based on the maximum mean discrepancy (MMD) to address the challenge of model specification. However, they ignore the presence of estimated para…
Risk Budgeting Portfolios: Existence and Computation
Adil Rengim Cetingoz, Jean-David Fermanian, Olivier Guéant
Modern portfolio theory has provided for decades the main framework for optimizing portfolios. Because of its sensitivity to small changes in input parameters, especially expected…
Sparse M-estimators in semi-parametric copula models
Jean-David Fermanian, Benjamin Poignard
We study the large sample properties of sparse M-estimators in the presence of pseudo-observations. Our framework covers a broad class of semi-parametric copula models, for which t…
Identifiability and estimation of meta-elliptical copula generators
Alexis Derumigny, Jean-David Fermanian
Meta-elliptical copulas are often proposed to model dependence between the components of a random vector. They are specified by a correlation matrix and a map , called density g…
Estimation of copulas via Maximum Mean Discrepancy
Pierre Alquier, Badr-Eddine Chérief-Abdellatif, Alexis Derumigny +1
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 pro…