Rousseau Judith - CV

Articles

Moss D., Rousseau J. (2024), Efficient Bayesian estimation and use of cut posterior in semiparametric hidden Markov models, Electronic Journal of Statistics, vol. 18, n°1, p. 1815-1886

Sulem D., Rivoirard V., Rousseau J. (2024), Bayesian estimation of nonlinear Hawkes processes, Bernoulli, vol. 30, n°2, p. 1257–1286

Naulet Z., Rousseau J., Caron F. (2024), Asymptotic Analysis of Statistical Estimators related to MultiGraphex Processes under Misspecification, Bernoulli, vol. 30, n°4, p. 2644-2675

Rockova V., Rousseau J. (2023), Ideal Bayesian Spatial Adaptation, Journal of the American Statistical Association

Robert C., Rousseau J. (2023), A special issue on Bayesian Inference: Challenges, Perspective, and Prospects, Philosophical Transactions. Physical, Mathematical and Engineering Sciences, vol. 381, n°2247

Caron F., Panero F., Rousseau J. (2023), On sparsity, power-law, and clustering properties of graphex processes, Advances in Applied Probability, vol. 55, n°4, p. 1211-1253

Browning R., Sulem D., Mengersen K., Rivoirard V., Rousseau J. (2021), Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19, PLoS ONE, vol. 16, n°4

Donnet S., Rivoirard V., Rousseau J. (2020), Nonparametric Bayesian estimation for multivariate Hawkes processes, Annals of Statistics, vol. 48, n°5, p. 2698-2727

Frazier D., Robert C., Rousseau J. (2020), Model Misspecification in ABC: Consequences and Diagnostics, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 82, n°2, p. 421-444

Frazier D., Martin G., Robert C., Rousseau J. (2018), Asymptotic Properties of Approximate Bayesian Computation, Biometrika, vol. 105, n°3, p. 593-607

Gassiat E., Rousseau J., Vernet E. (2018), Efficient semiparametric estimation and model selection for multidimensional mixtures, Electronic Journal of Statistics, vol. 12, n°1, p. 703-740

Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2018), Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures, Bernoulli, vol. 24, n°1, p. 231-256

Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2017), Posterior concentration rates for counting processes with Aalen multiplicative intensities, Bayesian Analysis, vol. 12, n°1, p. 53-87

Robert C., Rousseau J. (2017), How Principled and Practical Are Penalised Complexity Priors?, Statistical Science, vol. 32, n°1, p. 36-40

Naulet Z., Rousseau J. (2017), Posterior concentration rates for mixtures of normals in random design regression, Electronic Journal of Statistics, vol. 11, n°2, p. 4065-4102

Gassiat E., Rousseau J. (2016), Nonparametric finite translation hidden Markov models and extensions, Bernoulli, vol. 22, n°1, p. 193-212

Robert C., Rousseau J. (2016), Nonparametric Bayesian Clay for Robust Decision Bricks, Statistical Science, vol. 31, n°4, p. 506-510

Arbel J., Mengersen K., Rousseau J. (2016), Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity, Annals of Applied Statistics, vol. 10, n°3, p. 1496-1516

Rousseau J. (2016), On the Frequentist Properties of Bayesian Nonparametric Methods, Annual Reviews of Statistics and its applications, vol. 3:211-231, p. 24

Donnet S., Rousseau J. (2016), Bayesian Inference for Partially Observed Multiplicative Intensity Processes, Bayesian Analysis, vol. 11, n°1, p. 151-190

Hoffmann M., Rousseau J., Schmidt-Hieber J. (2015), On adaptive posterior concentration rates, Annals of Statistics, vol. 43, n°5, p. 2259-2295

Taeryon C., Rousseau J. (2015), A note on Bayes factor consistency in partial linear models, Journal of Statistical Planning and Inference, vol. 166, p. 158-170

van Havre Z., White N., Rousseau J., Mengersen K. (2015), Overfitting Bayesian Mixture Models with an Unknown Number of Components., PLoS ONE, vol. 10, n°7, p. e0131739

Castillo I., Rousseau J. (2015), A Bernstein-von Mises theorem for smooth functionals in semiparametric models, Annals of Statistics, vol. 43, n°6, p. 2353-2383

Petrone S., Scricciolo C., Rousseau J., Rizzelli S. (2014), Empirical Bayes methods in classical and Bayesian inference, Metron, vol. 72, n°2, p. 201-215

Rousseau J., Petrone S., Scricciolo C. (2014), Bayes and empirical Bayes : Do they merge?, Biometrika, vol. 101, n°2, p. 285-302

Hussein T., Alston C., Mengersen K., Rousseau J., Wraith D. (2014), Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions, Annals of Applied Statistics, vol. 8, n°1, p. 232-258

Rousseau J., Pillai N., Marin J-M., Robert C. (2014), Relevant statistics for Bayesian model choice, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 76, n°5, p. 833-859

Rousseau J., Gassiat E. (2014), About the posterior distribution in hidden Markov models with unknown number of states, Bernoulli, vol. 20, n°4, p. 2039-2075

Arbel J., Gayraud G., Rousseau J. (2013), Bayesian Optimal Adaptive Estimation Using a Sieve Prior, Scandinavian Journal of Statistics, vol. 40, n°3, p. 549-570

Robert C., Rousseau J., Gelman A. (2013), Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin, Statistics & Risk Modeling, vol. 30, n°2, p. 105-120

Rousseau J., Chopin N., Liseo B. (2013), Computational aspects of Bayesian spectral density estimation, Journal of Computational and Graphical Statistics, vol. 22, n°3, p. 533-557

Kruijer W., Rousseau J. (2013), Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors, Electronic Journal of Statistics, vol. 7, p. 2947-2969

Rousseau J., Rivoirard V. (2012), Bernstein–von Mises theorem for linear functionals of the density, Annals of Statistics, vol. 40, n°3, p. 1489-1523

Mengersen K., Guihenneuc-Jouyaux C., Low-Choy S., Rousseau J., Albert I., Donnet S. (2012), Combining Expert Opinions in Prior Elicitation, Bayesian Analysis, vol. 7, n°3, p. 503-532

Rousseau J., Chopin N., Liseo B. (2012), Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process, Annals of Statistics, vol. 40, n°2, p. 964-995

Lieberman O., Rosemarin R., Rousseau J. (2012), Asymptotic Theory for Maximum Likelihood Estimation of the Memory Parameter in Stationary Gaussian Processes, Econometric Theory, vol. 28, n°2, p. 457-470

Rivoirard V., Rousseau J. (2012), Posterior concentration rates for infinite dimensional exponential families, Bayesian Analysis, vol. 7, n°2, p. 311-334

Rousseau J., Mengersen K. (2011), Asymptotic behaviour of the posterior distribution in overfitted mixture models, Journal of the Royal Statistical Society. Series B, Statistical Methodology, vol. 73, n°5, p. 689-710

Rousseau J. (2010), Rates of convergence for the posterior distributions of mixtures of Betas and adaptive nonparametric estimation of the density, Annals of Statistics, vol. 38, n°1, p. 146-180

Mengersen K., Rousseau J., Mcvinish R. (2009), Bayesian Goodness-of-Fit Testing with Mixtures of Triangular Distributions, Scandinavian Journal of Statistics, vol. 36, p. 337-354

Rousseau J., Deman P., Guerquin-Kern J-L., Di Wu T., Elleaume H., Gouget B. (2009), Intracerebral delivery of 5-iodo-2'-deoxyuridine in combination with synchrotron stereotactic radiation for the therapy of the F98 glioma., Journal of Synchrotron Radiation, vol. 16, n°Pt 4, p. 573-581

Robert C., Chopin N., Rousseau J. (2009), Rejoinder: Harold Jeffreys' Theory of Probability Revisited, Statistical Science, vol. 24, n°2, p. 191-194

Robert C., Rousseau J., Chopin N. (2009), Harold Jeffreys' Theory of Probability revisited, Statistical Science, vol. 24, n°2, p. 141-172

Mcvinish R., Allingham D., Rousseau J., Nur D., Mengersen K. (2009), Bayesian hidden Markov Model for DNA segmentation : A prior sensitivity analysis, Computational Statistics & Data Analysis, vol. 53, n°5, p. 1873-1882

Grenier E., Rousseau J., Denis J-B., Albert I. (2008), Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases, Risk Analysis, vol. 28, n°2, p. 557-571

Chambaz A., Rousseau J. (2008), Bounds for Bayesian order identification with application to mixtures, Annals of Statistics, vol. 36, n°2, p. 938-962

Low-Choy S., Mengersen K., Rousseau J. (2008), Encoding expert opinion on skewed non-negative distributions, Journal of Applied Probability and Statistics , vol. 3, n°1, p. 1-21

Rousseau J., Fraser D. (2008), Studentization and deriving accurate p-values, Biometrika, vol. 95, n°1, p. 1-16

Gayraud G., Rousseau J. (2007), Consistency results on nonparametric Bayesian estimation of level sets using spatial priors, Test, vol. 16, n°1, p. 90-108

Gayraud G., Rousseau J. (2005), Rates of Convergence for a Bayesian Level Set Estimation, Scandinavian Journal of Statistics, vol. 32, n°4, p. 639-660

Rousseau J., Parmigiani G., Robert C., Müller P. (2004), Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays, Journal of the American Statistical Association, vol. 99, n°468, p. 990-1001

Chapitres d'ouvrage

Mengersen K., Johnson H., White N., Silburn P., Rousseau J. (2012), Hidden Markov models for complex stochastic processes: A case study in electrophysiology., in Pettitt, Anthony N., Case Studies in Bayesian Statistical Modelling and Analysis Wiley, p. 598

Marin J-M., Robert C., Rousseau J. (2011), Bayesian Inference and Computation, in Stumpf, Michael, Handbook of Statistical Systems Biology Wiley, p. 600

Robert C., Rousseau J. (2010), On Bayesian Data Analysis, in Böcker, Klaus, Rethinking Risk Measurement and Reporting, London: Infopro Digital Risk Ltd, p. 527

Communications avec actes

Rousseau J., Salomond J-B., Scricciolo C. (2014), On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models, in , ESAIM - Journée MAS 2012, Clermont-Ferrand, ESAIM: Proceedings and Surveys, 159-171 p.

Rousseau J. (2007), Approximating Interval hypothesis : p-values and Bayes factors, in West, M., Valencia International Meeting on Bayesian Statistics 2006, Oxford, Oxford University Press, 688 p.

Communications sans actes

Rosa P., Borovitskiy V., Terenin A., Rousseau J. (2023), Posterior Contraction Rates for Matérn Gaussian Processes on Riemannian Manifolds, NeurIPS 2023, La Nouvelle-Orléans, États-Unis

Arbel J., Mengersen K., Rousseau J. (2014), On diversity under a Bayesian nonparametric dependent model, SIS 2014, Cagliari, Italie

Rousseau J. (2014), On consistency issues in Bayesian nonparametric testing - a review, SIS 2014, Cagliari, Italie

Donnet S., Rousseau J., Rivoirard V. (2014), Non parametric Bayesian estimation for Hawkes processes, International Society for Bayesian Analysis World Meeting, ISBA 2014, Cancun, Mexique

Donnet S., Rousseau J., Rivoirard V., Scricciolo C. (2014), On Convergence Rates of Empirical Bayes Procedures, SIS 2014, Cagliari, Italie

Rousseau J., Mengersen K., Low-Choy S., Murray J. (2010), How Should We Combine Expert Opinions: On Elicitation, Encoding, Priors or Posteriors?, 9th Valencia International Meeting on Bayesian Statistics - 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Espagne

Khazaei S., Rousseau J. (2010), Bayesian Nonparametric Inference of Decreasing Densities, 42èmes Journées de Statistique, Marseille, France

Mengersen K., Rousseau J. (2010), Asymptotic Behaviour of the Posterior Distribution in Mixture Models with too many Components, 9th Valencia International Meeting on Bayesian Statistics - 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Espagne

Kruijer W., Rousseau J. (2010), On Bayesian Estimation of the Long-Memory Parameter in the FEXP-Model for Gaussian Time Series, 9th Valencia International Meeting on Bayesian Statistics - 2010 World Meeting of the International Society for Bayesian Analysis, Benidorm, Espagne

Rousseau J. (2009), Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparamatric estimation of the density, 7th Workshop on Bayesian Nonparametrics, Moncalieri, Italie

Rousseau J., Van Der Vaart A., Kruijer W. (2009), Adaptive Bayesian Density Estimation with Location-Scale Mixtures, 7th Workshop on Bayesian Nonparametrics, Moncalieri, Italie

Albert I., Grenier E., Denis J-B., Rousseau J. (2007), A global Bayesian approach for quantitative risk assessment (QRA) from farm to illness - application to campylobacteriosis through broiler, 5th International Conference of Predictive Modelling in Food, Athenes, Grèce

Prépublications / Cahiers de recherche

Naulet Z., Rousseau J., Caron F. (2022), Asymptotic Analysis of Statistical Estimators related to MultiGraphex Processes under Misspecification, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 79 p.

Hairault A., Robert C., Rousseau J. (2022), Evidence estimation in finite and infinite mixture models and applications, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 43 p.

Kamary K., Mengersen K., Robert C., Rousseau J. (2017), Testing hypotheses via a mixture estimation model, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 37 p.

Robert C., Rousseau J. (2016), Some comments about A Bayesian criterion for singular models by M. Drton and M. Plummer, Paris, Cahier de recherche CEREMADE, Université Paris Dauphine-PSL, 4 p.

Rousseau J., Alquier P., Chopin N., Cottet V. (2014), Bayesian matrix completion: prior specification and consistency, Paris, Université Paris-Dauphine, 26 p.

Donnet S., Rivoirard V., Rousseau J., Scricciolo C. (2014), Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures. Supplementary material, Paris, Université Paris-Dauphine, 4 p.

Rousseau J., Gassiat E. (2013), Non parametric finite translation mixtures with dependent regime, Paris, Université Paris-Dauphine, 26 p.

Taeryon C., Rousseau J. (2012), Bayes factor consistency in regression problems, Paris, Université Paris-Dauphine, 22 p.

Robert C., Marin J-M., Pillai N., Rousseau J. (2011), Evaluating statistic appropriateness for Bayesian model choice, Paris, Université Paris-Dauphine, 18 p.

Kruijer W., Rousseau J. (2011), Adaptive Bayesian Estimation of a spectral density, Paris, Université Paris-Dauphine, 14 p.

Liseo B., Rousseau J. (2006), Bayesian nonparametric estimation of the spectral density of a long memory Gaussian time series, Paris, Cahiers du CEREMADE, 51 p.

Fraser D., Rousseau J. (2005), Developing p-values: a Bayesian-frequentist convergence, Paris, Cahiers du CEREMADE, 16 p.

McVinish R., Mengersen K., Rousseau J. (2005), Bayesian Mixtures of Triangular distributions with application to Goodness-of-Fit Testing, Paris, Cahiers du CEREMADE, 45 p.

Robert C., Rousseau J. (2002), A Mixture Approach to Bayesian Goodness of Fit, Paris, Université Paris-Dauphine, 25 p.