DÉPARTEMENTDEMATHÉMATIQUESD'ORSAY
Publications |
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J. Capitao-Miniconi, E. Gassiat, Luc Lehéricy Deconvolution of repeated measurements corrupted by unknown noise soumis, 2024. I. Kaddouri, Z. Naulet, E. Gassiat, On the impossibility of detecting a late change-point in the preferential attachment random graph model soumis, 2024. A. Chaussart, A. Bonnet, E. Gassiat, S. Le Corff, Tree-based variational inference for Poisson log-normal models soumis, 2024. E. Gassiat, G. Stoltz, The van Trees inequality in the spirit of Hajek and Le Cam Statistical Science, à paraitre. E. Gassiat, S. Le Corff, Variational excess risk bound for general state space models Transactions on Machine Learning Research, à paraitre.. E. Aubinais, E. Gassiat, P. Piantanida, Fundamental limits of membership inference attacks on machine learning models soumis, 2023. E. Gassiat, I. Kaddouri, Z. Naulet, Clustering and classification risks in non-parametric hidden Markov and i.i.d. models soumis, 2023. K. Abraham, E. Gassiat, Z. Naulet, Frontiers to the learning of nonparametric hidden Markov models soumis, 2023. J. Capitao-Miniconi, E. Gassiat, L. Lehéricy, Support and distribution inference from noisy data soumis, 2023. M. Chagneux, E. Gassiat, P. Cloaguen, S. Le Corff, Additive smoothing error in backward variational inference for general state-space models Journal of Machine Learning Research, 25, 1-33, 2024. J. Capitao-Miniconi, E. Gassiat, Deconvolution of spherical data corrupted with unknown noise Electronic Journal of Statistics (17), 1, 607-649, 2023. K. Abraham, E. Gassiat, Z. Naulet, Fundamental limits for learning hidden Markov model parameters IEEE Trans. Info. th (69), 3, 1777-1794, 2023. H. Hälvä, S. Le Corff, L. Lehéricy, J. So, Y. Zhu, E. Gassiat, A. Hyvarinen, Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA Advances in Neural Information Processing Systems (NeurIPS), 2021. J. Ollion, C. Ollion, E. Gassiat, L. Lehéricy, S. Le Corff, Joint self-supervised blind denoising and noise estimation soumis, 2021. K. Abraham, I. Castillo, E. Gassiat, Multiple Testing in Nonparametric Hidden Markov Models: An Empirical Bayes Approach Journal of Machine Learning Research (23), 1-57, 2022. E. Gassiat, L. Lehéricy, S. Le Corff Deconvolution with unknown noise distribution is possible for multivariate signals Annals of Statistics, 50(1), 303-323, 2022. Version ArXiv E. Gassiat, L. Lehéricy, S. Le Corff Identifiability and consistent estimation of nonparametric translation hidden Markov models with general state space Journal of Machine Learning Research, 21(115), 1-40, 2020. E. Gassiat Universal Coding and Order Identification by Model Selection Methods Springer Monographs in Mathematics, 2018. B. Auder, E. Gassiat, M.A. Loum Least squares moment identification of binary regression mixture models Metrika, 84(4), 561-593, 2021. M.A. Loum, M.-A. Poursat, A. Sow, A. A. Sall, C. Loucoubar, E. Gassiat Multinomial logistic model for coinfection diagnosis between arbovirus and malaria in Kedougou International Journal of Biostatistics, 15 (2), 2019. E. Gassiat Mixtures of Nonparametric Components and Hidden Markov Models Chapitre du livre Handbook of Mixture Analysis (ed. G. Celeux, S. Fruhwirth-Schnatter, C. Robert) CRC Press, 2019. F. Koladjo, M. Ohannessian, E. Gassiat A truncation model for estimating species richness International Journal of Biostatistics, 15 (2), 2019. A. Ben-Hamou, S. Boucheron, E. Gassiat Pattern coding meets Censoring: (almost) Adaptive coding on countable alphabets soumis, 2016. E. Gassiat, J. Rousseau, E. Vernet Efficient semiparametric estimation and model selection for multidimensional mixtures Electronic Journal of Statistics, 18 (1), 703-740, 2018. N. Verzelen, E. Gassiat Adaptive estimation of High-Dimensional Signal-to-Noise Ratios Bernoulli, 24 (4B), 3683-3710, 2018. Y. de Castro, E. Gassiat, S. Le Corff Consistent estimation of the filtering and smoothing distributions in non-parametric hidden Markov models IEEE Trans. Info. th, 63 (8), 4758-4777, 2017. A. Bonnet, C. Lévy-Leduc, E. Gassiat, R. Toro, T. Bourgeron Improving heritability estimation by a variable selection approach in sparse high dimensional linear mixed models Journal of the Royal Statistical Society Series C, 67 (4), 813-839, 2018. Y. de Castro, E. Gassiat, C. Lacour Minimax adaptive estimation of non-parametric hidden Markov models Journal of Machine Learning Research, 17, 111 (43p), 2016. E. Gassiat Codage universel et identification d'ordre par sélection de modèles Cours spécialisés de la SMF, 2014. A. Bonnet, E. Gassiat, C. Lévy-Leduc Heritability estimation in high dimensional linear mixed models Electronic Journal of Statistics, 9, 2, 2099–2129, 2015. S. Boucheron, E. Gassiat, M. Ohannessian About adaptive coding on countable alphabets : Max-stable envelope classes IEEE Trans. Info. th, 61, 9, 4948–4967, 2015. E. Gassiat, A. Cleynen, S. Robin Inference in finite state space non parametric hidden Markov models and applications Statistics and Computing, 26 (1-2), 61-71, 2016. E. Gassiat, J. Rousseau Non parametric finite translation hidden Markov models and extensions Bernoulli, 22(1), 193-212, 2016. E. Gassiat, J. Rousseau About the posterior distribution in hidden Markov models with unknown number of states Bernoulli 20 (4), 2039-2075, 2014. E. Gassiat, R. van Handel The local geometry of finite mixtures Transactions of the AMS, 2, 366, 1047-1072, 2014. D. Bontemps, S. Boucheron, E. Gassiat About adaptive coding on countable alphabets IEEE Trans. Info. th, 60, 2, 808-821, 2014. E. Gassiat, R. van Handel Consistent order estimation and minimal penalties IEEE Trans. Info. th, 59, 2, 1115-1128, 2013. A. Galves, A. Garivier, E. Gassiat Joint estimation of intersecting context tree models Scandinavian Journal of Statistics, 40, 344-362, 2013. D. Bontemps, S. Boucheron, E. Gassiat Adaptive compression against a countable alphabet AofA'12, 201–218, Discrete Math. Theor. Comput. Sci. Proc., AQ, Assoc. Discrete Math. Theor. Comput. Sci., Nancy, 2012. R. Douc, E. Gassiat, B. Landelle, E. Moulines Forgetting of the initial distribution for non ergodic Hidden Markov Chains Annals of Applied Probability, 20, 1638-1662, 2010. W. Toussile, E. Gassiat Model Based Clustering using multilocus data with loci selection Advances in Data Analysis and Classification, 3, 109-134, 2009. S. Boucheron, E. Gassiat A Bernstein-von Mises Theorem for discrete probability distributions Electronic Journal of Statistics, 3, 114-148, 2009. J.-M. Azais, E. Gassiat, C. Mercadier The likelihood ratio test for general mixture models with possibly structural parameter. ESAIM P&S, 13, 301-327, 2009. S. Boucheron, A. Garivier, E. Gassiat Coding on countably infinite alphabets IEEE Trans. Info. th, 55, 358-373, 2009. A. Chambaz, A. Garivier, E. Gassiat A MDL approach to HMM with Poisson and Gaussian emissions. Application to order indentification. Journal of Statistical Planning and Inference, 139, 3, 962-977, 2009. E. Gassiat, B. Landelle Semiparametric regression estimation using noisy nonlinear non invertible functions of the observations soumis, 2008. G. Mahiane, C. Legeai, D. Taljaard, A. Latouche, A. Puren, A. Peillon, J. Bretagnolle, P. Ndong Nguema, E. Gassiat, B. Auvert Transmission probabilities of HIV and HSV-2, effect of male circumcision and interaction: a longitudinal study in a township of South Africa AIDS, 2008. A. Arribas-Gil, E. Gassiat, C. Matias Parameter estimation in pair hidden Markov models. Scandinavian Journal of Statistics, 33, 4, 651-671, 2006. S. Boucheron, E. Gassiat Error exponents for AR order testing. IEEE Trans. Info. th, 52, 472--488, 2006. J.-M. Azais, E. Gassiat, C. Mercadier E. Gassiat, C. Levy-Leduc |
| nEfficient semi-parametric estimation of the periods in a superposition of periodic functions with unknown shape. |
| Journal of Time Series Analysis, 27, 6, 877-910, 2006. |
| S. Boucheron, E. Gassiat |
| An information-theoretic perspective on Order Estimation. |
| Chapter 15 "Inference in
Hidden Markov Models" edited by O. Cappé and T. Ryden.,
Springer, 2005. |
| E. Nédélec, T. Moncion, E. Gassiat, B. Bossart, G. Duchateau-Nguyen, A. Denise, M. Termier |
| A pairwise alignment algorithm which favours clusters of blocks |
| Journal of Computational Biology, 12, 1, 2005. |
| E. Gassiat, S. Boucheron |
| Optimal error exponents in hidden Markov model order estimation |
| IEEE Trans. Info. th., 48, 964-980, 2003. |
| E. Gassiat |
| Likelihood ratio inequalities with applications to various mixtures |
| Ann. Inst.Henri Poincaré, 38, 897-906, 2002. |
| B. Bercu, E. Gassiat, E. Rio |
| Concentration inequalities, large and moderate deviations for self-normalized empirical processes |
| Annals of Proba., 30, 1576-1604, 2002. |
| E. Gassiat, C. Kéribin |
| The likelihood ratio test for the number of components in a mixture with Markov regime |
| ESAIM P&S, 2000. |
| I. Csiszar, F. Gamboa, E. Gassiat |
| M.E.M. pixel correlated solutions for generalized moment and interpolation problems |
| IEEE Trans. Info. th., 45, 2253-2271, 1999. |
| E. Moulines, J.F. cardoso , E. Gassiat |
| Maximum likelihood for blind separation and deconvolution of noisy signals using mixture models |
| ICASSP 97 |
| E. Gassiat, E. Gautherat |
| Speed of convergence for the blind deconvolution of a linear system with discrete random input |
| Annals of Stat., 27, 1999. |
D. Dacunha-Castelle, E. Gassiat
Testing the order of a model using locally
conic parametrization: population mixtures and stationary ARMA processes
Annals of Stat., 27, 4, 1178-1209, 1999.
D. Dacunha-Castelle, E. Gassiat
Testing in locally conic models and application to mixture models
ESAIM P et S, 1, 1997.
E. Gassiat, F. Gamboa
Source Separation when the input sources are discrete or have
constant
modulus
IEEE Trans. Signal Processing, 45 , 1997.
E. Gassiat, E. Gautherat
Identification of noisy linear systems with discrete random input
IEEE Trans. Inf. Theory, 44, 1941-1952, 1998.
E. Gassiat
Déconvolution aveugle de systèmes linéaires
discrets bruités
CRAS 319, série I, 489--492, 1994.
E. Gassiat, D. Dacunha-Castelle
Estimation of the number of components in a mixture
Bernoulli 3, 279-299, 1997.
E. Gassiat, F. Gamboa
Bayesian methods and Maximum entropy for ill posed inverse
problems
Annals of Statistics, 25, 328-350, 1997.
E. Gassiat, F. Gamboa
Blind deconvolution of discrete linear systems
Annals of Statistics, 24, 1964-1981, 1996.
E. Gassiat, F. Gamboa
Sets of superconcentration and the maximum entropy method on the
mean
SIAM Journal on Mathematical Analysis, 27, 1129-1152, 1996.
E. Gassiat, F. Gamboa
The maximum entropy method on the mean : applications to linear
programming and superresolution
Mathematical Programming, serie A, 66, 103--122, 1994.
E. Gassiat
Adaptive estimation in non causal stationary AR processes
Annals of Statistics, 21, 4, 2022--2042, 1993.
E. Gassiat, P. Doukhan
Quadratic deviation of penalized mean squares regression estimates
Journal of Mult. Analysis, 41, 89--101, 1992.
E. Gassiat, F. Monfront, Y. Goussart
On simultaneous signal estimation and parameter identification using
a generalized likelihood approach
IEEE Trans. on Inf. Theory , 38-1, 157--162, 1992.
E. Gassiat, F. Gamboa
Maximum d'entropie et problème des moments : cas
multidimensionnel
Prob. and Math. Stat., 12, 1, 67--83, 1991.
E. Gassiat
Problème des moments et concentration de mesure
CRAS. 310,I,41--44, 1990.
E. Gassiat
Semi-parametric estimation of a stationary non necessary causal
AR process with infinite variance
Journal of Multivariate Analysis, 32, 1,161--170, 1990.
E. Gassiat
Estimation semi-paramétrique d'un modèle
autorégressif
stationnaire multi-indice non nécessairement causal
Ann. Inst.Henri Poincaré, 26, 1,181-205, 1990.