Redefine statistical significance DJ Benjamin, JO Berger, M Johannesson, BA Nosek, EJ Wagenmakers, ... Nature human behaviour 2 (1), 6-10, 2018 | 1965 | 2018 |

Optimal sample size for multiple testing: the case of gene expression microarrays P Müller, G Parmigiani, C Robert, J Rousseau Journal of the American Statistical Association 99 (468), 990-1001, 2004 | 299 | 2004 |

Asymptotic behaviour of the posterior distribution in overfitted mixture models J Rousseau, K Mengersen Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2011 | 275 | 2011 |

Harold Jeffreys’s theory of probability revisited CP Robert, N Chopin, J Rousseau Statistical Science 24 (2), 141-172, 2009 | 166 | 2009 |

Relevant statistics for Bayesian model choice JM Marin, NS Pillai, CP Robert, J Rousseau Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2014 | 138 | 2014 |

Adaptive Bayesian density estimation with location-scale mixtures W Kruijer, J Rousseau, A Van Der Vaart Electronic Journal of Statistics 4, 1225-1257, 2010 | 133 | 2010 |

Combining expert opinions in prior elicitation I Albert, S Donnet, C Guihenneuc-Jouyaux, S Low-Choy, K Mengersen, ... Bayesian Analysis 7 (3), 503-532, 2012 | 124 | 2012 |

Bernstein–von Mises theorem for linear functionals of the density V Rivoirard, J Rousseau The Annals of Statistics 40 (3), 1489-1523, 2012 | 108 | 2012 |

On the impact of the activation function on deep neural networks training S Hayou, A Doucet, J Rousseau International conference on machine learning, 2672-2680, 2019 | 106 | 2019 |

A Bernstein–von Mises theorem for smooth functionals in semiparametric models I Castillo, J Rousseau The Annals of Statistics 43 (6), 2353-2383, 2015 | 102 | 2015 |

Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density J Rousseau The Annals of Statistics 38 (1), 146-180, 2010 | 79 | 2010 |

Asymptotic properties of approximate Bayesian computation DT Frazier, GM Martin, CP Robert, J Rousseau Biometrika 105 (3), 593-607, 2018 | 78 | 2018 |

On adaptive posterior concentration rates M Hoffmann, J Rousseau, J Schmidt-Hieber The Annals of Statistics 43 (5), 2259-2295, 2015 | 75 | 2015 |

Testing hypotheses via a mixture estimation model K Kamary, K Mengersen, CP Robert, J Rousseau arXiv preprint arXiv:1412.2044, 2014 | 73 | 2014 |

Quantitative risk assessment from farm to fork and beyond: A global Bayesian approach concerning food‐borne diseases I Albert, E Grenier, JB Denis, J Rousseau Risk Analysis: An International Journal 28 (2), 557-571, 2008 | 68 | 2008 |

On the selection of initialization and activation function for deep neural networks S Hayou, A Doucet, J Rousseau arXiv preprint arXiv:1805.08266, 2018 | 64 | 2018 |

Bayes and empirical Bayes: do they merge? S Petrone, J Rousseau, C Scricciolo Biometrika 101 (2), 285-302, 2014 | 61 | 2014 |

Bayesian optimal adaptive estimation using a sieve prior J Arbel, G Gayraud, J Rousseau Scandinavian journal of statistics 40 (3), 549-570, 2013 | 58 | 2013 |

Posterior concentration rates for infinite dimensional exponential families V Rivoirard, J Rousseau Bayesian Analysis 7 (2), 311-334, 2012 | 56 | 2012 |

Overfitting Bayesian mixture models with an unknown number of components Z Van Havre, N White, J Rousseau, K Mengersen PloS one 10 (7), e0131739, 2015 | 52 | 2015 |