Judith Rousseau

Q: What Schools Are Affiliated With Judith Rousseau

Judith Rousseau is affiliated with the following schools: Paris Sciences et Lettres University, University of Oxford

Judith Rousseau's AcademicInfluence.com Rankings

Judith Rousseau

Mathematics

#6642

World Rank

#9159

Historical Rank

Statistics

#838

World Rank

#928

Historical Rank

mathematics Degrees

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Mathematics

Why Is Judith Rousseau Influential?

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According to Wikipedia, Judith Rousseau is a Bayesian statistician who studies frequentist properties of Bayesian methods. She is a professor of statistics at the University of Oxford, a Fellow of Jesus College, Oxford, a Fellow of the Institute of Mathematical Statistics, and a Fellow of the International Society for Bayesian Analysis.

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Judith Rousseau's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Redefine statistical significance (2017) (1806)
Asymptotic behaviour of the posterior distribution in overfitted mixture models (2011) (275)
Optimal Sample Size for Multiple Testing (2004) (173)
Relevant statistics for Bayesian model choice (2011) (145)
Optimal Sample Size for Multiple Testing : the Case of Gene Expression Mi roarraysPeter (2004) (139)
On the Impact of the Activation Function on Deep Neural Networks Training (2019) (124)
Adaptive Bayesian density estimation with location-scale mixtures (2010) (122)
Harold Jeffreys’s Theory of Probability Revisited (2008) (122)
Combining expert opinions in prior elicitation (2010) (112)
BERNSTEIN-VON MISES THEOREM FOR LINEAR FUNCTIONALS OF THE DENSITY (2009) (107)
A Bernstein–von Mises theorem for smooth functionals in semiparametric models (2013) (89)
Asymptotic properties of approximate Bayesian computation (2016) (82)
Rates of convergence for the posterior distributions of mixtures of betas and adaptive nonparamatric estimation of the density (2010) (78)
On adaptive posterior concentration rates (2013) (77)
Testing hypotheses via a mixture estimation model (2014) (75)
Bayes and empirical Bayes : Do they merge? (2012) (71)
On the Selection of Initialization and Activation Function for Deep Neural Networks (2018) (67)
Quantitative Risk Assessment from Farm to Fork and Beyond: A Global Bayesian Approach Concerning Food‐Borne Diseases (2008) (58)
Bayesian Optimal Adaptive Estimation Using a Sieve Prior (2012) (53)
Overfitting Bayesian Mixture Models with an Unknown Number of Components (2015) (53)
Posterior concentration rates for infinite dimensional exponential families (2012) (50)
Asymptotic behaviour of the empirical Bayes posteriors associated to maximum marginal likelihood estimator (2015) (47)
Bayesian Goodness of Fit Testing with Mixtures of Triangular Distributions (2009) (45)
Model misspecification in approximate Bayesian computation: consequences and diagnostics (2020) (44)
Posterior concentration rates for empirical Bayes procedures, with applications to Dirichlet Process mixtures (2014) (41)
A Mixture Approach to Bayesian Goodness of Fit (2002) (41)
On Bayesian Data Analysis (2010) (40)
Approximating Interval hypothesis : p-values and Bayes factors (2007) (39)
Valid Asymptotic Expansions for the Maximum Likelihood Estimator of the Parameter of a Stationary, Gaussian, Strongly Dependent Process (2002) (37)
Studentization and deriving accurate p-values (2008) (33)
On the Frequentist Properties of Bayesian Nonparametric Methods (2016) (32)
Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks (2019) (31)
About the posterior distribution in hidden Markov models with unknown number of states (2012) (30)
Nonparametric finite translation hidden Markov models and extensions (2016) (27)
Model Misspecification in ABC: Consequences and Diagnostics (2017) (25)
A General Bernstein--von Mises Theorem in semiparametric models (2013) (21)
Empirical Bayes methods in classical and Bayesian inference (2014) (21)
Asymptotic frequentist coverage properties of Bayesian credible sets for sieve priors (2016) (20)
Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity (2014) (19)
ASYMPTOTIC THEORY FOR MAXIMUM LIKELIHOOD ESTIMATION OF THE MEMORY PARAMETER IN STATIONARY GAUSSIAN PROCESSES (2011) (19)
Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis (2009) (18)
Nonparametric Bayesian estimation for multivariate Hawkes processes (2018) (18)
Simple discrete-time self-exciting models can describe complex dynamic processes: A case study of COVID-19 (2020) (18)
Stable ResNet (2020) (17)
Bayesian Nonparametrics for Sparse Dynamic Networks (2016) (17)
Bayesian nonparametric estimation of the spectral density of a long or intermediate memory Gaussian process (2010) (16)
Probability Matching Priors: Higher Order Asymptotics (2006) (15)
Laplace Expansions in Markov chain Monte Carlo Algorithms (2005) (15)
Coverage Properties of One-Sided Intervals in the Discrete Case and Application to Matching Priors (2000) (14)
VALID EDGEWORTH EXPANSION FOR THE SAMPLE AUTOCORRELATION FUNCTION UNDER LONG RANGE DEPENDENCE (2001) (14)
Inherent difficulties of non-Bayesian likelihood-based inference, as revealed by an examination of a recent book by Aitkin (2010) (14)
Rates of Convergence for a Bayesian Level Set Estimation (2005) (14)
Nonasymptotic Bounds for Bayesian Order Identification with Application to Mixtures (2008) (14)
Bayesian matrix completion: prior specification (2014) (14)
SMALL-SAMPLE LIKELIHOOD-BASED INFERENCE IN THE ARFIMA MODEL (2000) (13)
Computational Aspects of Bayesian Spectral Density Estimation (2012) (13)
Adaptive Supremum Norm Posterior Contraction : Spike-and-Slab Priors and Anisotropic Besov Spaces (2017) (13)
A note on Bayes factor consistency in partial linear models (2015) (11)
On sparsity and power-law properties of graphs based on exchangeable point processes (2017) (11)
Non-informative priors in the case of Gaussian long-memory processes (2002) (11)
Adaptive Supremum Norm Posterior Contraction: Wavelet Spike-and-Slab and Anisotropic Besov Spaces (2017) (10)
Non parametric finite translation mixtures with dependent regime (2013) (10)
Adaptive density estimation based on a mixture of Gammas (2016) (9)
Efficient semiparametric estimation and model selection for multidimensional mixtures (2016) (9)
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel (2019) (9)
Bayesian matrix completion: prior specification and consistency (2014) (8)
On sparsity, power-law and clustering properties of graphex processes (2017) (8)
Posterior Concentration Rates for Counting Processes with Aalen Multiplicative Intensities (2014) (7)
Bayesian semi-parametric estimation of the long-memory parameter under FEXP-priors (2012) (7)
Encoding expert opinion on skewed non-negative distributions (2008) (7)
Rejoinder: Harold Jeffreys’s Theory of Probability Revisited (2009) (7)
BOUNDS FOR BAYESIAN ORDER IDENTIFICATION WITH APPLICATION TO MIXTURES By (2021) (6)
Integrated objective Bayesian estimation and hypothesis testing : a discussion (2010) (6)
BAYESIAN NONPARAMETRIC DEPENDENT MODEL FOR THE STUDY OF DIVERSITY FOR SPECIES DATA (2014) (6)
Bayes factor consistency in regression problems (2012) (6)
Bayesian estimation of nonlinear Hawkes process (2021) (5)
Asymptotic Theory for Maximum Likelihood Estimation in Stationary Fractional Gaussian Processes, Under Short-, Long- and Intermediate Memory (2009) (5)
Discussion of “Frequentist coverage of adaptive nonparametric Bayesian credible sets” (2015) (5)
Use in practice of importance sampling for repeated MCMC for Poisson models (2010) (5)
Ideal Bayesian Spatial Adaptation (2021) (4)
Bayesian Mixtures of Triangular distributions with application to Goodness-of-Fit Testing (2005) (4)
Sparse Networks with Core-Periphery Structure (2019) (4)
Nonparametric Bayesian Estimation of Level Sets (2002) (3)
DEVELOPING p-VALUES: A BAYESIAN-FREQUENTIST CONVERGENCE (2005) (3)
On the asymptotic behaviour of the posterior distribution in hidden Markov Models with unknown number of states (2014) (3)
Estimating a density near an unknown manifold: a Bayesian nonparametric approach (2022) (3)
Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement (2022) (3)
USING INFORMATIVE PRIORS IN THE ESTIMATION OF MIXTURES OVER TIME WITH APPLICATION TO AEROSOL PARTICLE SIZE DISTRIBUTIONS (2014) (3)
Adaptive Bayesian Estimation of a spectral density (2011) (3)
Asymptotic Behaviour of the Posterior Distribution in Mixture Models with too many Components (2010) (3)
How Principled and Practical Are Penalised Complexity Priors (2017) (3)
Overfitting hidden Markov models with an unknown number of states (2016) (3)
Bayesian Inference and Computation (2011) (3)
Recentered Importance Sampling With Applications to Bayesian Model Validation (2013) (3)
Bayesian nonparametric estimation of the spectral density of a long memory Gaussian time series (2007) (3)
Efficient Bayesian estimation and use of cut posterior in semiparametric hidden Markov models (2022) (3)
Harold Jeffreys' Theory of Probability revisited: a reply (2009) (2)
Bayesian hypothesis testing as a mixture estimation modelT1 (2021) (2)
On Convergence Rates of Empirical Bayes Procedures (2014) (2)
Do we need an integrated Bayesian/likelihood inference? (2010) (2)
Bayesian Mixture Models: Theory and Methods (2019) (2)
Bayesian Nonparametric Inference of Decreasing Densities (2010) (2)
On some aspects of the asymptotic properties of Bayesian approaches in nonparametric and semiparametric models (2014) (1)
Posterior concentration rates for mixtures of normals in random design regression (2017) (1)
On diversity under a Bayesian nonparametric dependent model (2014) (1)
Asymptotic Analysis of Statistical Estimators related to MultiGraphex Processes under Misspecification (2021) (1)
Clustering action potential spikes: Insights on the use of overfitted finite mixture models and Dirichlet process mixture models (2016) (1)
Asymptotic properties of HPD regions in the discrete case (2002) (1)
Nonparametric Bayesian Clay for Robust Decision Bricks (2016) (1)
Some comments about A Bayesian criterion for singular models by M. Drton and M. Plummer (2016) (1)
Some comments about James Watson's and Chris Holmes' "Approximate Models and Robust Decisions" (2016) (1)
Laplace expansions in MCMC algorithms (2005) (1)
Consistency results on nonparametric Bayesian estimation of level sets using spatial priors (2007) (1)
Wasserstein convergence in Bayesian deconvolution models (2021) (1)
Evaluating statistic appropriateness for Bayesian model choice (2011) (1)
Asymptotic Bayes risks for a general class of losses (1997) (1)
Laplace Expansions in MCMC Algorithms for Latent Variable Models (2002) (1)
Redefine statistical significance (2017) (0)
Biometrika Trust Printed in Great Britain Bayes and empirical Bayes : do they merge ? (2017) (0)
Empirical Bayes methods in classical and Bayesian inference (2014) (0)
Identification of Pre-Clinical Alzheimer's Disease in a Population of Elderly Cognitively Normal Participants. (2019) (0)
Evidence estimation in finite and infinite mixture models and applications (2022) (0)
Sample Size Choice for Microarray Experiments (2006) (0)
A flexible, random histogram kernel for discrete-time Hawkes processes (preprint) (2022) (0)
Bayesian Inference for Partially Observed Branching Processes (2016) (0)
On the Error Rate for Asymptotic Chi-Squared Distributions in the Lattice Case (2002) (0)
Exact Convergence Rates of the Neural Tangent Kernel in the Large Depth Limit (2019) (0)
Bayesian Inference for Partially Observed Multiplicative Intensity Processes (2016) (0)
Some comments about “ Penalising model component complexity : A principled , practical approach to constructing priors ” by Simpson , Rue , Martins , Riebler , and Sørbye (2016) (0)
Foundations of Bayesian Inference for Complex Statistical Models (2022) (0)
Scalable Variational Bayes methods for Hawkes processes (2022) (0)
Probability Matching Priors: Higher Order Asymptotics. Gauri Sankar Datta and Rahul Mukerjee (2006) (0)
SUPPLEMENTARY MATERIAL OF NONPARAMETRIC BAYESIAN ESTIMATION FOR MULTIVARIATE HAWKES PROCESSES (2019) (0)
Tails assumptions and posterior concentration rates for mixtures of Gaussians (2016) (0)
Bayesian hidden Markov Model for DNA segmentation : A prior sensitivity analysis (2018) (0)
On consistency issues in Bayesian nonparametric testing - a review (2014) (0)
Non parametric Bayesian estimation for Hawkes processes (2014) (0)
Bayesian Spike Sorting: Parametric and Nonparametric Multivariate Gaussian Mixture Models (2020) (0)
Asymptotic Expansions for Long-Memory Stationary Gaussian Processes (2003) (0)
1 Title : Redefine Statistical Significance (2017) (0)
7th Workshop on Bayesian Nonparametrics (2009) (0)
An importance sampling approach to sensitivity analysis of priors in Bayesian analysis of DNA sequence segmentation (2007) (0)
On Bayesian Estimation of the Long-Memory Parameter in the FEXP-Model for Gaussian Time Series (2010) (0)
Combining elicited expert knowledge into Bayesian priors (2013) (0)
How Should We Combine Expert Opinions: On Elicitation, Encoding, Priors or Posteriors? (2010) (0)
Some comments about "Penalising model component complexity" by Simpson et al. (2017) (2016) (0)
Laplace Expansions in Markov Chain Monte (2016) (0)
1406 . 4406 v 1 Posterior concentration rates for empirical Bayes procedures with applications to Dirichlet process mixtures SOPHIE (2016) (0)
Submitted to the Annals of Statistics ADAPTIVE BAYESIAN DENSITY ESTIMATION WITH LOCATION-SCALE MIXTURES By (2009) (0)
H. Rouanet & al., "New ways in statistical methodology", Bern, Peter Lang: European University Studies, Series 6, Psychology, vol. 618, 1998, 276 p. (2000) (0)
Comment on Article by Berger, Bernardo, and Sun (2015) (0)
Hidden Markov models for complex stochastic processes: A case study in electrophysiology (2012) (0)
Bayesian Inference (2010) (0)
Submitted to the Annals of Statistics BAYESIAN SEMI-PARAMETRIC ESTIMATION OF THE LONG-MEMORY PARAMETER UNDER FEXP-PRIORS : SUPPLEMENTARY MATERIAL (2012) (0)
Submitted to the Annals of Statistics BAYESIAN SEMI-PARAMETRIC ESTIMATION OF THE LONG-MEMORY PARAMETER UNDER FEXP-PRIORS : SUPPLEMENTARY MATERIAL (2012) (0)
A special issue on Bayesian inference: challenges, perspectives and prospects (2023) (0)
Studentization and the determination of p-values (2008) (0)

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Other Resources About Judith Rousseau

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What Schools Are Affiliated With Judith Rousseau?

Judith Rousseau is affiliated with the following schools: