6 papers
A smoothed semiparametric likelihood for estimation of nonparametric finite mixture models with a copula-based dependence structure
Michael Levine, Gildas Mazo
In this manuscript, we consider a finite multivariate nonparametric mixture model where the dependence between the marginal densities is modeled using the copula device. Pseudo EM…
Regularizing nested Monte Carlo Sobol' index estimators to balance the trade-off between explorations and repetitions in global sensitivity analysis of stochastic models
Henri Mermoz Kouye, Gildas Mazo
Sobol' sensitivity index estimators for stochastic models are functions of nested Monte Carlo estimators, which are estimators built from two nested Monte Carlo loops. The outer lo…
Performing global sensitivity analysis on simulations of a continuous-time Markov chain model motivated by epidemiology
Henri Mermoz Kouye, Gildas Mazo, Clémentine Prieur +1
In this paper we apply a methodology introduced in Navarro Jimenez et al (2016) in the framework of chemical reaction networks to perform a global sensitivity analysis on simulatio…
Inference on extremal dependence in the domain of attraction of a structured Hüsler-Reiss distribution motivated by a Markov tree with latent variables
Stefka Asenova, Gildas Mazo, Johan Segers
A Markov tree is a probabilistic graphical model for a random vector indexed by the nodes of an undirected tree encoding conditional independence relations between variables. One p…
Parametric versus nonparametric: the fitness coefficient
Gildas Mazo, François Portier
The fitness coefficient, introduced in this paper, results from a competition between parametric and nonparametric density estimators within the likelihood of the data. As illustra…
Extending one-factor copulas
Nathan Uyttendaele, Gildas Mazo
So far, one-factor copulas induce conditional independence with respect to a latent factor. In this paper, we extend one-factor copulas to conditionally dependent models. This is a…