29 citations · 73 across the 15 of their papers we have counts for
8 papers · 1 filter
Reconciling Bayesian and frequentist approaches to robustness against outliers
Philippe Gagnon, Alain Desgagné
Heavy-tailed models are used as a way to gain robustness against outliers in Bayesian analyses. In frequentist analyses, M-estimators are often employed. In this paper, the two app…
Robust heavy-tailed versions of generalized linear models with applications in actuarial science
Philippe Gagnon, Yuxi Wang
Generalized linear models (GLMs) form one of the most popular classes of models in statistics. The gamma variant is used, for instance, in actuarial science for the modelling of cl…
Robustness against conflicting prior information in regression
Philippe Gagnon
Including prior information about model parameters is a fundamental step of any Bayesian statistical analysis. It is viewed positively by some as it allows, among others, to quanti…
Optimal scaling of random-walk Metropolis algorithms using Bayesian large-sample asymptotics
Sebastian M Schmon, Philippe Gagnon
High-dimensional limit theorems have been shown useful to derive tuning rules for finding the optimal scaling in random-walk Metropolis algorithms. The assumptions under which weak…
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…
Weak Convergence and Optimal Tuning of the Reversible Jump Algorithm
Philippe Gagnon, Mylène Bédard, Alain Desgagné
The reversible jump algorithm is a useful Markov chain Monte Carlo method introduced by Green (1995) that allows switches between subspaces of differing dimensionality, and therefo…
Bayesian Robustness to Outliers in Linear Regression and Ratio Estimation
Alain Desgagné, Philippe Gagnon
Whole robustness is a nice property to have for statistical models. It implies that the impact of outliers gradually vanishes as they approach plus or minus infinity. So far, the B…