64 citations · 107 across the 5 of their papers we have counts for
6 papers
Adaptive Pseudo-Marginal Algorithm
Sarra Abaoubida, Mylène Bédard, Florian Maire
The Pseudo-Marginal (PM) algorithm is a popular Markov chain Monte Carlo (MCMC) method used to sample from a target distribution when its density is inaccessible, but can be estima…
The Linear Lasso: a location model resolution
D. A. S. Fraser, Mylène Bédard
We use location model methodology to guide the least squares analysis of the Lasso problem of variable selection and inference. The nuisance parameter is taken to be an indicator f…
An automatic robust Bayesian approach to principal component regression
Philippe Gagnon, Mylène Bédard, Alain Desgagné
Principal component regression uses principal components as regressors. It is particularly useful in prediction settings with high-dimensional covariates. The existing literature t…
A New Bayesian Approach to Robustness Against Outliers in Linear Regression
Philippe Gagnon, Alain Desgagné, Mylène Bédard
Linear regression is ubiquitous in statistical analysis. It is well understood that conflicting sources of information may contaminate the inference when the classical normality of…
A Dirichlet Form approach to MCMC Optimal Scaling
Giacomo Zanella, Wilfrid S. Kendall, Mylène Bédard
This paper develops the use of Dirichlet forms to deliver proofs of optimal scaling results for Markov chain Monte Carlo algorithms (specifically, Metropolis-Hastings random walk s…
Weak convergence of Metropolis algorithms for non-i.i.d. target distributions
Mylène Bédard
In this paper, we shall optimize the efficiency of Metropolis algorithms for multidimensional target distributions with scaling terms possibly depending on the dimension. We propos…