2 citations · 2 across the 6 of their papers we have counts for
8 papers
Simulation-consistent Estimation of the Marginal Likelihood for Block Models
Martin Metodiev, Marie Perrot-Dockès, Guilhem Fouetillou +2
We propose a methodology for computing marginal likelihoods for block models. The proposed estimator computes the marginal likelihood from Markov chain Monte Carlo (MCMC) samples a…
Efficiently Computed Marginal Likelihoods for Multivariate Mixture Models
Martin Metodiev, Nicholas J. Irons, Marie Perrot-Dockès +2
We propose a new methodology for efficiently computing marginal likelihoods for univariate and multivariate mixture models. The estimator that this methodology proposes computes th…
A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +4
We consider the problem of estimating a high-dimensional covariance matrix from a small number of observations when covariates on pairs of variables are available and the variables…
Easily Computed Marginal Likelihoods from Posterior Simulation Using the THAMES Estimator
Martin Metodiev, Marie Perrot-Dockès, Sarah Ouadah +2
We propose an easily computed estimator of marginal likelihoods from posterior simulation output, via reciprocal importance sampling, combining earlier proposals of DiCiccio et al…
Post hoc false discovery proportion inference under a Hidden Markov Model
Marie Perrot-Dockès, Gilles Blanchard, Pierre Neuvial +1
We address the multiple testing problem under the assumption that the true/false hypotheses are driven by a Hidden Markov Model (HMM), which is recognized as a fundamental setting…
Some detection tests for low complexity data models and unknown background distribution
D. Mary, S. Bourguignon, E. Roquain +2
We consider several detection situations where, under the alternative hypothesis, the signal admits a low complexity model and, under both the null and the alternative hypotheses,…
Estimation of large block structured covariance matrices: Application to "multi-omic" approaches to study seed quality
Marie Perrot-Dockès, Céline Lévy-Leduc, Loïc Rajjou
Motivated by an application in high-throughput genomics and metabolomics, we propose a novel, efficient and fully data-driven approach for estimating large block structured sparse…
Variable selection in multivariate linear models with high-dimensional covariance matrix estimation
Marie Perrot-Dockès, Céline Lévy-Leduc, Laure Sansonnet +1
In this paper, we propose a novel variable selection approach in the framework of multivariate linear models taking into account the dependence that may exist between the responses…