29 citations · 73 across the 15 of their papers we have counts for
16 papers
Exponential Convergence of CAVI for Bayesian PCA
Arghya Datta, Philippe Gagnon, Florian Maire
Probabilistic principal component analysis (PCA) and its Bayesian variant (BPCA) are widely used for dimension reduction in machine learning and statistics. The main advantage of p…
Simple proof of robustness for Bayesian heavy-tailed linear regression models
Philippe Gagnon
In the Bayesian literature, a line of research called resolution of conflict is about the characterization of robustness against outliers of statistical models. The robustness char…
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…
Theoretical guarantees for lifted samplers
Philippe Gagnon, Florian Maire
Lifted samplers form a class of Markov chain Monte Carlo methods which has drawn a lot attention in recent years due to superior performance in challenging Bayesian applications. A…
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…
Improving multiple-try Metropolis with local balancing
Philippe Gagnon, Florian Maire, Giacomo Zanella
Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the appealing feature of being amenable to parallel computing. At each iteration, it samples several…
Theoretical properties of Bayesian Student- linear regression
Philippe Gagnon, Yoshiko Hayashi
Bayesian Student- linear regression is a common robust alternative to the normal model, but its theoretical properties are not well understood. We aim to fill some gaps by provi…
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 asymptotic Peskun ordering and its application to lifted samplers
Philippe Gagnon, Florian Maire
A Peskun ordering between two samplers, implying a dominance of one over the other, is known among the Markov chain Monte Carlo community for being a remarkably strong result. It i…
Informed reversible jump algorithms
Philippe Gagnon
Incorporating information about the target distribution in proposal mechanisms generally produces efficient Markov chain Monte Carlo algorithms (or at least, algorithms that are mo…
Non-reversible jump algorithms for Bayesian nested model selection
Philippe Gagnon, Arnaud Doucet
Non-reversible Markov chain Monte Carlo methods often outperform their reversible counterparts in terms of asymptotic variance of ergodic averages and mixing properties. Lifting th…
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…