Preprints

• F. Caron, J. Rousseau. On sparsity and power-law properties of graphs based on exchangeable point processes. arXiv:1708.03120, Aug. 2017. [Tech Report]
• David T. Frazier, Christian P. Robert and Judith Rousseau Model Misspecification in ABC: Consequences and Diagnostics. [Tech Report]
• S. Donnet, V. Rivoirard and J. Rousseau (2018) Nonparametric Bayesian estimation of multivariate Hawkes processes. [arxiv]
• Rousseau, J. and Szabó, B. Asymptotic frequentist coverage properties of Bayesian credible sets for sieve priors in general settings. . [Arxiv]
• Z. van Havre, J. Rousseau, N. White and K. Mengersen (2015) Overfitting hidden Markov models with an unknown number of states . [Arxiv]
• K. Kamary, K. Mengersen, C. Robert and J. Rousseau (2014) Testing hypotheses as a mixture estimation model .[Arxiv]
• Zoé van Havre, Nicole White, Judith Rousseau, Kerrie Mengersen (2015) Clustering action potential spikes: Insights on the use of overfitted finite mixture models and Dirichlet process mixture models.. [Arxiv]

International Journals & Conferences

2018

• D. Frazier, G. Martin, C.P. Robert   J. Rousseau Asymptotic properties of approximate Bayesian computations.  Biometrika, 2018, to appear. [arxiv]
• E. Gassiat, E. Vernet   J. Rousseau Efficient semiparametric estimation and model selection for multidimensional mixtures..  Electronic Journal of Statistics, vol. 12, pp 703-740. 2018. [Paper][Citation]
• S. Donnet, V. Rivoirard, J. Rousseau   C. Scricciolo Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures. .  Bernoulli, vol. 24, pp 231-256. 2018 [Paper]

2017
• N. Bochkina and J. Rousseau. Adaptive density estimation based on a mixture of Gammas. . Electronic Journal of Statistics, 11}, 916-962, 2017. [Paper][Bibtex]
• Z. Naulet and J. Rousseau (2017) Posterior concentration rates for mixtures of normals in random design regression. . Electronic Journal of Statistics, vol. 11, pp 4065–4102, 2017. [Paper][Bibtex]
• . Rousseau and B. Szabo. Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator.. Annals of Statistics , vol. 45, pp 833-865, 2017. [Paper][Bibtex]
  
• S. Donnet, V. Rivoirard, J. Rousseau and C. Scricciolo Posterior Concentration Rates for Counting Processes with Aalen Multiplicative Intensities. . Bayesian Analysis, vol. 12, pp. 53-87, 2017. [Paper][citation]

2016
• J. Arbel, K. Mengersen and J. Rousseau. Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity . The Annals of Applied Statistics, vol. 10, 1496-1516, 2016. [Publisher][Bibtex]
• J. Rousseau. On the frequentist properties of Bayesian nonparametric methods. . Annual Statistical Reviews, Vol. 3: 211–231, 2016. [Paper]
• E. Gassiat and J. Rousseau. Nonparametric finite translation hidden Markov models and extensions.. Bernoulli, vol. 22, 193-212, 2016. [Paper][citation]
  
• S. Donnet and J. Rousseau : Bayesian inference for partially observed branching processes.. Bayesian Anal.,vol. 11, 151-190, 2016. [Paper][citation]
  

2015
• I. Castillo and J. Rousseau (2015)A general Bernstein von Mises Theorem in semi-parametric models. . Annals of statistics,\textbf{43}, 2353-2383, 2015 [arxiv] [citation]
  
• M. Hoffman, J. Rousseau and Johannes Schmidt-HieberOn adaptive posterior concentration rates. . Annals of statistics,, vol. 43, pp 2259-2295 , 2015 [Paper] [citation] 
• Z. van Havre, N. White, K. Mengersen and J. Rousseau Overfitting Bayesian Mixture Models with an Unknown Number of Components, . . PLoS ONE , vol. 10, 2015. [paper]
• Choi, T. and Rousseau, J. A note on Bayes factor consistency in partial linear models. Journal of Statistical Planning and Inference, , vol. 166, 158-170. 2015. [Arxiv]

2014
• Wraith D, Mengersen K, Alston, C., Rousseau J. and Hussein T. : Using informative priors in the estimation of mixtures over time with application to aerosol particle size distribution.. Annals of applied statistics, vol. 8, 232-258, 2014. [arxiv]
• S. Petrone, J. Rousseau and C. Scriocciolo Bayes and empirical Bayes : Do they merge?. Biometrika,, vol. 101, 285-302, 2014. [arxiv]
• S. Petrone, S. Rizzeli, J. Rousseau and C. Scriocciolo Empirical Bayes methods in classical and Bayesian inference . Metron , vol. 72, 201–215 2014. [researchgate]
• J.M. Marin, N. Pillai, C.P. Robert and J. Rousseau Relevant statistics for Bayesian model choice. . JRSS B, vol. 6, 833–859, 2014 [arxiv]
• E. Gassiat and J. RousseauAbout the asymptotic behaviour of the posterior distribution in hidden Markov Models.. Bernoulli,vol. 20, 2039-2075, 2014. [paper]

2013
• W. Kruijer and J. Rousseau Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors. . Electronic Journ. Statist., vol. 7, pp 2947-2969, 2013 [Paper] [publi]
• N. Chopin, J. Rousseau and B. LiseoComputational aspects of Bayesian spectral density estimation.. JCGS . vol. 22,pp 533- 557, 2013 [ arxiv]
• J. Arbel, G. Gayraud and J. Rousseau Least Bayesian optimal estimation using a sieve prior.. Scandinavian J. Statist. . Vol.40, pp 549–570, 2013. [Publisher]

• F. Gelman, A., Robert, C.P., and Rousseau, J. Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin. Statistics & Risk Modeling, Vol. 30,pp 1001-1016 [ Techreport ]

2012
• Rivoirard, V. and Rousseau, J. (2012) Bernstein Von Mises Theorem for linear functionals of the density. Annals of Statistics , Vol. 40,pp. 1489-1523, 2012. [ arxiv]
• I. Albert, S. Donnet, C. Guihenneuc-Jouyaux, S. Low-Choi, K. Mengersen and J. Rousseau Combining expert opinions in prior elicitation. . Bayesian Analysis - discussed paper, ,Vol. 7, 503-532, 2012. [ paper ]
• J. Rousseau, N. Chopin and B. Liseo Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process. . Annals of Statistics, vol. 40,pp. 964-995, 2012. [ paper ]
• McVinish, R. Mengersen, K., Rousseau J., Nur, D. and Guihenneuc C. Recentered importance sampling with applications to Bayesian model validation. . Journ.Comput. Graph. Statist. DOI: 10.1080/10618600.2012.681239, 2012.
• V. Rivoirard and J. Rousseau Posterior concentration rates for infinite dimensional exponential families.. Bayesian Analysis, vol. 7, pp. 311-334, 2012 .
• Lieberman, O., R. Rosemarin, Rousseau, J. Asymptotic Theory for Maximum Likelihood Estimation of the memory parameter in stationary Gaussian processess . Econometric Theory, vol. 28, pp. 457-470, 2012.

2011
• J. Rousseau and K. Mengersen Asymptotic behaviour of the posterior distribution in overfitted models. J. Royal Statist. Soc. B vol. 73, pp. 689-710, 2011
• Rousseau, J. and Robert, C.P. Discussion on Consonni and LaRocca's On moment priors for Bayesian model choice . Bayesian Statistics 9.

2010
• W. Kruijer, J. Rousseau and A. van der Vaart Adaptive Bayesian Density Estimation with Location-Scale Mixtures . Electronic Journal of Statistics, vol. 4, pp. 1225-1257, 2010.
• D. Gadja, C. Guihenneuc, J. Rousseau, K. Mengersen and D. Nur Use in practice of importance sampling for repeated MCMC for Poisson models . Elect. Journ. Statist. vol. 4,pp. 361-383.
• Robert, C.P, Marin, J.M. and Rousseau, J. Bayesian inference . In Handbook of Statistical Systems Biology. 2010

2009
• Rousseau, J. Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparamatric estimation of the density. . Annals of Statistics, vol. 38,pp. 146-180, 2009
• C.P. Robert, N. Chopin and J. Rousseau . . Harold Jeffreys' theory of probability revisited. . Statistical Science, vol. 24,pp. 141-172, 2009
• R.McVinish, J. Rousseau and K. Mengersen Bayesian Goodness-of-Fit Testing with Mixtures of Triangular Distributions. Scandinavian journal of statistics , vol. 36, 337–354, 2009.

2008
• Nur Darfiana, Allingham David, Rousseau J, Mengersen K L and McVinish Bayesian analysis of DNA sequence segmentation: A prior sensitivity analysis . . Comp. Statist. Data Anal. , vol. 60,pp. 573–581, 2008.
• A. Chambaz and J. Rousseau (2008) Bounds for Bayesian order identification with application to mixtures. Annals of Statistics, vol. 36,pp. 938-962, 2008
• S.L. Choy, K. Mengersen, J. Rousseau Encoding Expert Opinion on Skewed Non-Negative Distributions. Journ. Appl. Probab. Statist., vol. 3,pp. 1-21, 2008.
• D. Fraser and J. Rousseau Studentization and the determination of p-values. Biometrika , vol.95,pp. 1-16, 2008

2007
• I. Albert, E. Grenier, J.B. Denis et J. Rousseau Quantitative Risk Assessement from Farm to Fork and Beyond: a global Bayesian approach concerning food-borne diseases . Biometrika , Risk Analysis , vol. 28,pp. 558-571, 2007.

2006
• J. Rousseau Approximating Interval hypothesis : p-values and Bayes factors . Bayesian Statistics 8, (J. M. Bernardo, J. O. Berger, A. P. Dawid and A. F. M. Smith, eds.) Oxford: University Press, 2006.
• P. Muller, C. Robert and J. Rousseau Sample Size Choice for Microarray Experiments In Bayesian Inference for Gene Expression and Proteomics . Bayesian Statistics 8 eds. K.-A. Do, P.Muller and M.Vannucci. Cambridge University Press. 2006
• C.P. Robert and J. Rousseau Comments on: Intrinsic credible regions: an objective Bayesian approach to interval estimation, by J. M. Bernardo.. TEST vol. 14, pp. 317-384, 2006.

2005
• G. Gayraud et J. Rousseau Rates of convergence for a Bayesian level set estimation . Scand. Journ. Statist., vol. 14, pp. 75-94, 2005.
• G. Gayraud et J. Rousseau Consistency results on nonparametric Bayesian estimation of level sets u ng spatial priors . Test 2005.
• C. Guihenneuc et J. Rousseau Laplace expansions in MCMC algorithms. Journal of Computational and Graphical Statistics , vol. 32,pp. 639-660, 2005.
2004
• P. Muller, G. Parimigiani, C. Robert et J. Rousseau Optimal sample sizes for multiple testing: the case of gene expression microarrays . J.A.S.A., T. & M , vol. 99,pp. 990-1001, 2004.
• J. Rousseau (2004) Discussion on : Bayesian inference for Elliptical linear models: Conjugate analysis and model comparison by C.R.B. Arellano-Valle, P. Iglesias, I. Vidal . Bayesian Statistics 7, eds J. M. Bernardo et al., Amsterdam: North Holland. 2004
• C. Robert et J. Rousseau (2004) Discussion on : Bayesian and Frequentist Multiple Testing, de C. Genovese et L. Wasserman . Bayesian Statistics 7, eds J. M. Bernardo et al.,Amsterdam: North Holland, 2004.

2004
• O. Lieberman, J. Rousseau, D. Zucker Valid asymptotic expansions for the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process . Annals of Statistics , vol. 31,pp. 586-612, 2003
• A. Philippe, J. Rousseau Non-informative priors for Gaussian long-memory processes, . Bernoulli, vol. 8, 451-473, 2003
• D. Zucker, J. Rousseau, A. Philippe et O. Lieberman Asymptotic expansions for long-memory stationary Gaussian processes. In . Foundations of Statistical Inference, Y. Haitovsky, H.R. Lerche, Y. Ritov (eds.). Physica-Verlag, Heidelberg. 2003

2003
• J. Rousseau Coverage properties of HPD regions in the discrete case, . Journal of Multivariate Analysis , vol. 83,pp. 1-21, 2003.

2001
• M. Ghosh, J. Rousseau, D. Kim Non-informative priors for the bivariate Fieller-Creasy problem, . Statistics and Decisions, vol. 19, 3-27, 2001
• O. Lieberman, J. Rousseau, D. Zucker Small sample asymptotics for the sample autocorrelation function under long-range dependence,. Econometric theory, vol. 17, 257-275, 2001.

2000
• O. Lieberman, J. Rousseau et D. Zucker Small sample Likelihood-based inference in the ARFIMA model,. Econometric theory, vol. 16, No. 2, 231-248 . 2000
• J. Rousseau Coverage properties of one-sided intervals in the discrete case and applications to matching priors, . Annals of the Institute of statistical mathematics, vol. 52 28-42, 2000
• J. Rousseau Asymptotic Bayes risk for a general class of losses, . Statistics and Probability Letters , vol. 35, 115-121, 1997
  Last update: Nov. 2017