Datta A., Gagnon P. and Maire F. Exponential Convergence of CAVI for Bayesian PCA.
Preprints
Gagnon P. and Desgagné A. On a fundamental difference between Bayesian and frequentist approaches to robustness.
Gagnon P. and Maire F. Theoretical guarantees for lifted samplers.
Publications
Gagnon P. and Maire F. (2024) An asymptotic Peskun ordering and its application to lifted samplers. Bernoulli.
Gagnon P. and Wang Y. (2024) Robust heavy-tailed versions of generalized linear models with applications in actuarial science. Computational Statistics & Data Analysis.
Gagnon P., Maire F. and Zanella G. (2023) Improving multiple-try Metropolis with local balancing. Journal of Machine Learning Research.
Gagnon P. (2023) Robustness against conflicting prior information in regression. Bayesian Analysis.
Gagnon P. and Hayashi Y. (2023) Theoretical properties of Bayesian Student-t linear regression. Statistics & Probability Letters.
Schmon S. M. and Gagnon P. (2022) Optimal scaling of random walk Metropolis algorithms using Bayesian large-sample asymptotics. Statistics and Computing.
Gagnon P. (2021) Informed reversible jump algorithms. Electronic Journal of Statistics.
Gagnon P. and Doucet A. (2021) Nonreversible jump algorithms for Bayesian nested model selection. Journal of Computational and Graphical Statistics.
Gagnon P., Bédard M. and Desgagné A. (2021) An automatic robust Bayesian approach to principal component regression. Journal of Applied Statistics.
Gagnon P., Desgagné A. and Bédard M. (2020) A new Bayesian approach to robustness against outliers in linear regression. Bayesian Analysis.
Gagnon P., Bédard M. and Desgagné A. (2019) Weak convergence and optimal tuning of the reversible jump algorithm. Mathematics and Computers in Simulation.
Desgagné A. and Gagnon P. (2019) Bayesian robustness to outliers in linear regression and ratio estimation. Brazilian Journal of Probability and Statistics.