Ghislaine Gayraud
Statut : Professeure des Universités
Bureau : GI 124
Research interests
- Bayesian nonparametric
- Minimax hypothesis testing
- High dimensional problems
- Modelling and statistical inference for dependent data
- Applications to gene regulatory networks
Projets
- AAP équipes projets ISCD : ``Num4Lyme’’ (2023 - 2026) ; PI : I. Maffuci, GEC-UTC
Co-responsabilité du développement de modèles stochastiques bio-inspirés par des molécules pour l’aide au diagnostic de la maladie de Lyme
- AAP UTC-AMI Covid 19 ``Coveille’’ (2020-2021) ;
PI : M. Davila-Felipe, LMAC-UTC
Veille de la propagation du virus au travers de la modélisation de la dynamique de l’épidémie du Covid-19 à différents niveaux de granularité
Travaux récents
- Bayolo Soler, G., Dávila Felipe, M., Gayraud, G. (2023). Test allocation based on risk of infection from first and second-order contact tracing. HAL-04267859
- Votsi, I., Gayraud, G., Barbu, V. S., Limnios, N. (2021). Hypotheses testing and posterior concentration rates for
semi-Markov processes, Stat. Inference Stoch. Process., 24, 707—732 - Wiecek, W., Bois F., Gayraud, G. (2019) Structure learning of Bayesian networks involving cyclic structures. Hal-02130362
- Zgheib, E., Gao, W., Limonciel, A., Aladjov, H., Yang, H., Tebby, C., Gayraud, G., Jennings, P., Sachana, M., Beltman, J.B., Bois, F.Y. (2019). Application of three approaches for quantitative AOP development to renal toxicity. Computational Toxicology, No. 11, 1—13
- Datta, S., Gayraud, G., Leclerc, E., Bois, F.Y. (2017) Graph_sampler : a simple tool for fully Bayesian analyses of DAG-models, Computational Statistics, 32, No. 2, 691—716.
- Butucea, C., Gayraud, G. (2016), Sharp detection of smooth signals in a high-dimensional sparse matrix with indirect observations, Ann. Inst. H. Poincaré Probab. Statist. "Probabilités & Statistiques", 52, No. 4, 1564—1591
- Bernardi, M., Gayraud, G., Petrella, L. (2015), Bayesian tail risk interdependence using quantile regression, Bayesian Analysis, 10, 553—603
- Bois, F., Gayraud, G. (2015), Probabilistic generation of random networks taking into account information on motifs occurrence, Journal of Computational Biology, 22, 25—36
- Arbel, J., Gayraud, G., Rousseau, J. (2013), Bayesian optimal adaptive estimation using a sieve prior, Scandinavian Journal of Statistics, 40, 549—570
- Gayraud, G., Ingster, Yu. (2012), Detection of sparse additive functions, Electronic Journal of Statistics, 6, 1409-1448
- Gayraud, G., Tribouley, K. (2011), A goodness-of-fit test for copula densities, Test, 20, 549—573
- Gayraud, G. (2008), To perform the convergence rate of the Bayesian level set estimate ?, Proceedings of Multimodality and Related Topics, Publication de l’Université Paris X-Nanterre.
- Gayraud, G., Rousseau, J. (2007), Consistency results on nonparametric Bayesian estimation of level sets using spatial priors, Test, 16, 90—108
- Gayraud, G., Rousseau, J. (2005), Rates of Convergence for a Bayesian Level set estimation, Scandinavian Journal of Statistics, 32, 639—660
- Gayraud, G., Pouet, Ch. (2005), Adaptive minimax testing in the discrete regression scheme, Probability Theory and Related Fields, 4, 531—558
- Gayraud, G. (2002), Minimax estimation of a discontinuity for the density, Journal of Nonparametric Statistics, 14, 59—66
- Gayraud, G., Tsybakov, A.B. (2002), Testing hypotheses about contours in images, Journal of Nonparametric Statistics, 14, 67—85
- Gayraud, G. (2001), Minimax hypotheses testing about the density support, Bernoulli, 7, 507—526
- Gayraud, G., Pouet, Ch. (2001), Minimax testing composite null hypotheses in the discrete regression scheme, Mathematical Methods of Statistics, 10, 375—394
- Gayraud, G., Tribouley, K. (1999), Adaptive Estimation and Confidence Interval for a Quadratic Functional by Wavelet, Statistic and probability letters, 44, 109—122
- Gayraud, G. (1997), Estimation of functionals of density support, Mathematical Methods of Statistics, 6, 26—47