Judith Rousseau

Judith Rousseau

Bayesian statistics, Asymptotics, Nonparametric statistics

I work on Bayesian statistics, trying to understand the connexions between Bayesian and frequentist methods in particular in complex or large dimensional models.

Publications

2018

  • D. T. Frazier , G. M. Martin , C. P. Robert , J. Rousseau , Asymptotic properties of approximate Bayesian computation, arXiv preprint arXiv:1607.06903, 2018.
  • S. Donnet , V. Rivoirard , J. Rousseau , C. Scricciolo , . others , Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures, Bernoulli, vol. 24, no. 1, 231–256, 2018.

2017

  • F. Caron , J. Rousseau , On sparsity and power-law properties of graphs based on exchangeable point processes, arXiv preprint arXiv:1708.03120, 2017.
  • S. Donnet , V. Rivoirard , J. Rousseau , C. Scricciolo , . others , Posterior concentration rates for counting processes with Aalen multiplicative intensities, Bayesian Analysis, vol. 12, no. 1, 53–87, 2017.
  • N. Bochkina , J. Rousseau , . others , Adaptive density estimation based on a mixture of Gammas, Electronic Journal of Statistics, vol. 11, no. 1, 916–962, 2017.
  • J. Rousseau , B. Szabo , Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator, Ann. Statist., vol. 45, no. 2, 833–865, Apr. 2017.

2016

  • J. Rousseau , On the frequentist properties of bayesian nonparametric methods, Annual Review of Statistics and Its Application, vol. 3, 211–231, 2016.
  • J. Arbel , K. Mengersen , J. Rousseau , . others , Bayesian nonparametric dependent model for partially replicated data: the influence of fuel spills on species diversity, The Annals of Applied Statistics, vol. 10, no. 3, 1496–1516, 2016.
  • E. Gassiat , J. Rousseau , . others , Nonparametric finite translation hidden Markov models and extensions, Bernoulli, vol. 22, no. 1, 193–212, 2016.

2015

  • Z. Havre , N. White , J. Rousseau , K. Mengersen , Overfitting Bayesian mixture models with an unknown number of components, PloS one, vol. 10, no. 7, e0131739, 2015.
  • I. Castillo , J. Rousseau , A Bernstein-von Mises theorem for smooth functionals in semiparametric models, Ann. Statist., vol. 43, no. 6, 2353–2383, Dec. 2015.
  • M. Hoffmann , J. Rousseau , J. Schmidt-Hieber , On adaptive posterior concentration rates, Ann. Statist., vol. 43, no. 5, 2259–2295, Oct. 2015.

2014

  • D. Wraith , K. Mengersen , C. Alston , J. Rousseau , T. Hussein , . others , Using informative priors in the estimation of mixtures over time with application to aerosol particle size distributions, The Annals of Applied Statistics, vol. 8, no. 1, 232–258, 2014.
  • E. Gassiat , J. Rousseau , . others , About the posterior distribution in hidden Markov models with unknown number of states, Bernoulli, vol. 20, no. 4, 2039–2075, 2014.
  • P. Alquier , V. Cottet , N. Chopin , J. Rousseau , Bayesian matrix completion: prior specification, arXiv preprint arXiv:1406.1440, 2014.
  • S. Petrone , S. Rizzelli , J. Rousseau , C. Scricciolo , Empirical Bayes methods in classical and Bayesian inference, Metron, vol. 72, no. 2, 201–215, 2014.
  • S. Petrone , J. Rousseau , C. Scricciolo , Bayes and empirical Bayes: do they merge?, Biometrika, vol. 101, no. 2, 285–302, 2014.
  • J. Marin , N. S. Pillai , C. P. Robert , J. Rousseau , Relevant statistics for Bayesian model choice, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 76, no. 5, 833–859, 2014.

2013

  • J. Arbel , G. Gayraud , J. Rousseau , Bayesian optimal adaptive estimation using a sieve prior, Scandinavian journal of statistics, vol. 40, no. 3, 549–570, 2013.
  • R. McVinish , K. Mengersen , D. Nur , J. Rousseau , C. Guihenneuc-Jouyaux , Recentered importance sampling with applications to Bayesian model validation, Journal of Computational and Graphical Statistics, vol. 22, no. 1, 215–228, 2013.
  • W. Kruijer , J. Rousseau , . others , Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors, Electronic Journal of Statistics, vol. 7, 2947–2969, 2013.
  • N. Chopin , J. Rousseau , B. Liseo , Computational aspects of Bayesian spectral density estimation, Journal of Computational and Graphical Statistics, vol. 22, no. 3, 533–557, 2013.

2012

  • V. Rivoirard , J. Rousseau , . others , Bernstein–von Mises theorem for linear functionals of the density, The Annals of Statistics, vol. 40, no. 3, 1489–1523, 2012.
  • V. Rivoirard , J. Rousseau , . others , Posterior concentration rates for infinite dimensional exponential families, Bayesian Analysis, vol. 7, no. 2, 311–334, 2012.
  • I. Albert , S. Donnet , C. Guihenneuc-Jouyaux , S. Low-Choy , K. Mengersen , J. Rousseau , . others , Combining expert opinions in prior elicitation, Bayesian Analysis, vol. 7, no. 3, 503–532, 2012.
  • J. Rousseau , N. Chopin , B. Liseo , . others , Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process, The Annals of Statistics, vol. 40, no. 2, 964–995, 2012.
  • O. Lieberman , R. Rosemarin , J. Rousseau , Asymptotic theory for maximum likelihood estimation of the memory parameter in stationary Gaussian processes, Econometric Theory, vol. 28, no. 2, 457–470, 2012.

2011

  • J. Rousseau , K. Mengersen , Asymptotic behaviour of the posterior distribution in overfitted mixture models, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol. 73, no. 5, 689–710, 2011.

2010

  • W. Kruijer , J. Rousseau , A. Van Der Vaart , . others , Adaptive Bayesian density estimation with location-scale mixtures, Electronic Journal of Statistics, vol. 4, 1225–1257, 2010.
  • J. Rousseau , . others , Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparametric estimation of the density, The Annals of Statistics, vol. 38, no. 1, 146–180, 2010.
  • D. Gajda , C. Guihenneuc-Jouyaux , J. Rousseau , K. Mengersen , D. Nur , . others , Use in practice of importance sampling for repeated MCMC for Poisson models, Electronic journal of statistics, vol. 4, 361–383, 2010.

2009

  • C. P. Robert , N. Chopin , J. Rousseau , . others , Harold Jeffreys’s theory of probability revisited, Statistical Science, vol. 24, no. 2, 141–172, 2009.
  • R. Mcvinish , J. Rousseau , K. Mengersen , Bayesian goodness of fit testing with mixtures of triangular distributions, Scandinavian Journal of Statistics, vol. 36, no. 2, 337–354, 2009.
  • D. Nur , D. Allingham , J. Rousseau , K. L. Mengersen , R. McVinish , Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis, Computational Statistics & Data Analysis, vol. 53, no. 5, 1873–1882, 2009.

2008

  • I. Albert , E. Grenier , J. Denis , J. Rousseau , Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food-Borne Diseases, Risk Analysis, vol. 28, no. 2, 557–571, 2008.
  • D. Fraser , J. Rousseau , Studentization and deriving accurate p-values, Biometrika, vol. 95, no. 1, 1–16, 2008.
  • A. Chambaz , J. Rousseau , Bounds for Bayesian order identification with application to mixtures, The Annals of Statistics, 938–962, 2008.
  • S. J. Low Choy , K. L. Mengersen , J. Rousseau , Encoding expert opinion on skewed non-negative distributions, Journal of Applied Probability and Statistics, vol. 3, no. 1, 1–21, 2008.

2007

  • J. Rousseau , Approximating interval hypothesis: p-values and Bayes factors, Bayesian statistics, vol. 8, 417–452, 2007.

2005

  • G. Gayraud , J. Rousseau , Rates of convergence for a Bayesian level set estimation, Scandinavian journal of statistics, vol. 32, no. 4, 639–660, 2005.
  • C. Guihenneuc-Jouyaux , J. Rousseau , Laplace expansions in Markov chain Monte Carlo algorithms, Journal of Computational and Graphical Statistics, vol. 14, no. 1, 75–94, 2005.

2004

  • P. Müller , G. Parmigiani , C. Robert , J. Rousseau , Optimal sample size for multiple testing: the case of gene expression microarrays, Journal of the American Statistical Association, vol. 99, no. 468, 990–1001, 2004.

2003

  • O. Lieberman , J. Rousseau , D. M. Zucker , . others , Valid asymptotic expansions for the maximum likelihood estimator of the parameter of a stationary, Gaussian, strongly dependent process, The Annals of Statistics, vol. 31, no. 2, 586–612, 2003.

2002

  • A. Philippe , J. Rousseau , . others , Non-informative priors in the case of Gaussian long-memory processes, Bernoulli, vol. 8, no. 4, 451–473, 2002.
  • J. Rousseau , Asymptotic properties of HPD regions in the discrete case, Journal of multivariate analysis, vol. 83, no. 1, 1–21, 2002.

2001

  • O. Lieberman , J. Rousseau , D. M. Zucker , Valid Edgeworth expansion for the sample autocorrelation function under long range dependence, Econometric Theory, vol. 17, no. 1, 257–275, 2001.
  • J. Rousseau , M. Ghosh , D. Kim , Non-informative priors for the bivariate Fieller-Creasy problem, Statistics and Decisions, vol. 19, 227, 2001.

2000

  • O. Lieberman , J. Rousseau , D. M. 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 application to matching priors, Annals of the Institute of Statistical Mathematics, vol. 52, no. 1, 28–42, 2000.