Judith ROUSSEAU

Grade : Professeur des Universités Paris Dauphine


Bureau:
E32
Timbre:
J120
Lieu:
(MK1)
Labo:
LS

Téléphone :

Mail : rousseau[arrowbase]ensae.fr

ResearchEducationJobsBooksJournal articlesLinks

Research Interests

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  • Bayesian statistics :

Default Bayesian analysis
Nonparametric Bayesian statistics
Bayesian testing

 

  • Interraction between Bayesian and frequentist approaches

Frequentist properties of Bayesian methods
Asymptotic analysis

  • Mixture distributions

  • MCMC algorithms

 

  • Prior elicitation

 


Biography

Education

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1997 : Phd at the University Paris VI : "Asymptotic properties of bayes estimates" ; Supervisor : Christian Robert, Professor at the University of Paris IX,


Jobs

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Professor at Université Paris Dauphine (CEREMADE) and at ENSAE.

 Other responsibilities

 Associate editor of :
Annals of Statistics since 2009 link
ANZJS since 2008   link
Bernoulli since 2013   link
ISBA:
Member of the board of ISBA link
IMS :
Programm secretary of the IMS link
SMAI :
Member of the Neveu prize committee

 

Former positions

Maitre de conférence at Université Paris Descartes. 1998-2003



Publications

Books

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Journal articles

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1. Theoretical papers

  • J.M. Marin, N. Pillai, C.P. Robert and J. Rousseau (2013) Relevant statistics for Bayesian model choice. To appear in JRSS B
  • E. Gassiat and J. Rousseau (2013) : On the asymptotic behaviour of the posterior distribution in hidden Markov Models. To appear in Bernoulli

  • S. Petrone, J. Rousseau and C. Scriocciolo (2013)  :  Bayes and empirical Bayes : Do they merge? To appear in Biometrika
  • J. Arbel, G. Gayraud and J. Rousseau (2013) Bayesian optimal estimation using a sieve prior.  To appear, Scandinavian J. Statist.
  • V. Rivoirard and J. Rousseau (2012) Bernstein-von Mises Theorem for linear functionals of the density Annals of Statistics, 40, 1489-152
  • V. Rivoirard and J. Rousseau (2012) Posterior concentration rates for infinite dimensional exponential families. Bayesian Analysis
  • J. Rousseau, N. Chopin and B. Liseo (2011) Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process. Ann. Statist.
  • J. Rousseau and K. Mengersen (2011)  Asymptotic behaviour of the posterior distribution in overfitted models. JRSSB
  • Lieberman, O., Rosemarin, R. and Rousseau, J. (2009) : Asymptotic Theory for Maximum Likelihood Estimation of the Memory Parameter in Stationary Gaussian Processess. Econometric theory
  • W. Kruijer, J. Rousseau and A. van der Vaart (2010) Adaptive Bayesian Density Estimation with Location-Scale Mixtures Electronic Journal of Statistics 
  • J. Rousseau (2009) : Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparamatric estimation of the density. Annals of Statistics, 
  • C.P. Robert, N. Chopin and J. Rousseau (2009) : Harold Jeffreys' theory of probability revisited.  Statistical Science
  • R.McVinish, J. Rousseau and K. Mengerson (2008): Bayesian Goodness-of-Fit Testing with Mixtures of Triangular Distributions. Scandinavian journal of statistics 
  • D. Fraser and J. Rousseau (2008) : Studentization and the determination of p-values. Biometrika 95, 1-16. 
  • A. Chambaz and J. Rousseau (2008): Bounds for Bayesian order identification with application to mixtures. Annals of Statistics, 36, 938-962  
  • J. Rousseau (2007): 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. 
  • G. Gayraud and J. Rousseau (2005): Rates of convergence for a Bayesian level set estimation. Scand. Journ. Statist. 14, 1, 75-94.
  • G. Gayraud and J. Rousseau (2005): Consistency results on nonparametric Bayesian estimation of level sets using spatial priors. test  
  • C. Guihenneuc et J. Rousseau (2005): Laplace expansions in MCMC algorithms for latent variable models. Journal of Computational and Graphical Statistics. 32 (4), 639-660.
  • O. Lieberman, J. Rousseau, D. Zucker (2003): Valid asymptotic expansions for the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process, Annals of Statistics, 31, 586-612.
  • A. Philippe, J. Rousseau (2003) : Non-informative priors for Gaussian long-memory processes, Bernoulli,  8, 451-473.
  • D. Zucker, J. Rousseau, A. Philippe et O. Lieberman (2003) : Asymptotic expansions for long-memory stationary Gaussian processes. In Foundations of Statistical Inference, Y.Haitovsky, H.R. Lerche, Y. Ritov (eds.). Physica-Verlag, Heidelberg.
  • J. Rousseau (2002) : Coverage properties of HPD regions in the discrete case, Journal of Multivariate Analysis, 83,1-21.
  • M. Ghosh, J. Rousseau, D. Kim (2001) : Non-informative priors for the bivariate Fieller-Creasy problem, Statistics and Decisions, 19, No. 3, 227.
  • O. Lieberman, J. Rousseau, D. Zucker (2001) : Small sample asymptotics for the sample autocorrelation function under long-range dependence, Econometric theory, 17, No.1, 257-275.
  • O. Lieberman, J. Rousseau et D. Zucker (2000) : Small sample Likelihood-based inference in the ARFIMA model, Econometric theory, 16, No. 2, 231-248 .
  • J. Rousseau (2000) : Coverage properties of one-sided intervals in the discrete case and applications to matching priors, Annals of the Institute of statistical mathematics, 52, no 1, 28-42.
  • J. Rousseau (1997) : Asymptotic Bayes risk for a general class of losses, Statistics and Probability Letters, 35, 115-121.

2. Methodological Papers.

    • N. Chopin, J. Rousseau and B. Liseo (2013) Computational aspects of Bayesian spectral density estimation. JCGS - to appear
    • R. Mc Vinish, K. Mengersen,  J. Rousseau,   D. Nur and C. Guihenneuc, (2011): Recentered importance sampling with applications to Bayesian Model validation.  Journ. Comp. Graph. Statist.
    • D. Gadja, C. Guihenneuc, J. Rousseau, K. Mengersen and D. Nur (2010): Use in practice of importance sampling for repeated MCMC for Poisson models.  Elect. Journ. Statist.

    • P. Muller, C. Robert and J. Rousseau (2006) : Sample Size Choice for Microarray Experiments In Bayesian Inference for Gene Expression and Proteomics. (eds. K.-A. Do, P.Muller and M.Vannucci). Cambridge University Press.
    • P. Muller, G. Parimigiani, C. Robert et J. Rousseau (2004): Optimal sample sizes for multiple testing: the case of gene expression microarrays. J.A.S.A., T. & M, 99, 990-1001.


 3. Applied papers

    • Wraith D, Mengersen K, Rousseau J. and Hussein T. (2013) Using informative priors in the estimation of mixtures over time with application to aerosol particle size distribution. Annals of applied statistics.
    • D. Nur, D. Allingham, J. Rousseau, K. Mengersen and R. McVinish (2008 : Bayesian analysis of DNA sequence segmentation: A prior sensitivity analysis . Comp. Statist. Data Anal.
    • S.L. Choy, K. Mengersen, J. Rousseau (2008): Encoding Expert Opinion on Skewed Non-Negative Distributions. Journ.  Appl. Probab. Statist. 3
    • I. Albert, E. Grenier, J.B. Denis et J. Rousseau (2006) : Quantitative Risk Assessement from Farm to Fork and Beyond: a global Bayesian approach concerning food-borne diseases.  Risk Analysis. 28:2, 558-571.
    • Nur Darfiana, Allingham David, Rousseau J, Mengersen K L and McVinish (2007): Bayesian analysis of DNA sequence segmentation: A prior sensitivity analysis . Comp. Statist. Data Anal. 

4.  Discussions

    • Robert, C.P. and Rousseau (2011) J. Discussion on Bernardo's Integrated objective Bayesian estimation and hypothesis testing. Bayesian Statistics 9
    • Rousseau, J. and Robert, C.P. (2011) Discussion on Consonni and LaRocca's On moment priors for Bayesian model choice.  Bayesian Statistics 9
    • J. Rousseau (2004): Discussion sur : 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 {\it al.}, Amsterdam: North Holland.
    • C. Robert et J. Rousseau (2004) : Discussion sur : Bayesian and Frequentist Multiple Testing, de C. Genovese et L. Wasserman. Bayesian Statistics 7, eds J. M. Bernardo et al.,Amsterdam: North Holland.

5. Book chapters

  • N. White, H. Johnson, P. Silburn, J. Rousseau and K. Merngersen (2012) : Hidden Markov models for complex stochastic processes: A case study in electrophysiology. In Case Studies in Bayesian Statistical Modelling and Analysis
  • Robert, C.P. and Rousseau, J.(2009): On Bayesian Data Analysis .  In "Bayesian Methods and Expert Elicitation", Risk Book, London
  • Robert, C.P, Marin, J.M. and Rousseau, J. (2010)  Bayesian inference. In  Handbook of Statistical Systems Biology

 

6. Preprints

  •   Z. van Havre, N. White, K. Mengersen and J. Rousseau (2013) Investigating the number of components in an overfitted Gaussian mixture model.
  • I. Castillo and J. Rousseau (2013). A general Bernstein von Mises Theorem in semi-parametric models. 
  • M. Hoffmann, J. Rousseau and J. Schmidt-Hieber (2013). On adaptive posterior concentration rates.
  • J. Rousseau, J-B. Salomond and C. Scricciolo (2013) On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models
  • E. Gassiat and J. Rousseau (2013) : Nonparametric finite translation mixtures with dependent regimes
  • S. Donnet and J. Rousseau (2013) : Bayesian inference for partially observed branching processes.
  • W. Kruijer and J. Rousseau (2012) : Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors.
  • W. Kruijer and J. Rousseau (2012) : Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors - Supplementary material.
  • J. Rousseau and T. Choi (2011) Bayes factor consistency in regression problems.
  • S. Khazaei, J. Rousseau and F. Balabdaoui (2010) Nonparametric Bayesian estimation of densities under monotonicity constraint
  •  
  • J. Rousseau and W. Kruijer (2011) Adaptive Bayesian Estimation of a spectral density.
  • Wraith D, Mengersen K, Rousseau J. and Hussein T. (2009) A Bayesian hierarchical mixture model for aerosol particle size distributions
  • C. Robert et J. Rousseau (2003): Bayesian Goodness of Fit -A Mixture Approach to Bayesian Goodness of Fit.
  • J. Rousseau and I. Skovgaard (2003): On the error rate for asymptotic chi-squared distributions in the lattice case.
  • J. Rousseau (1997): Asymptotic coverages of joint two-sided intervals.

 

 

 

 



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 Page personnelle  : www.ceremade.dauphine.fr/~rousseau/ (the links to some of my papers can be found there)


"Le centre de la Recherche en Économie et Statistique ne peut être tenu responsable pénalement des infractions aux lois que pourrait contenir cette page personnelle qui est sous la responsabilité de son auteur."