Preprints R Passeggeri adn O Wintenberger Extremes for stationary regularly varying random fields over arbitrary index sets.
 P Maillard and O Wintenberger Moment conditions for random coefficient AR(∞) under non-negativity assumptions.
 A. Godichon-Baggioni, N. Werge and O. Wintenberger Learning from time-dependent streaming data with online stochastic algorithms.
 J. de Vilmarest and O. Wintenberger Viking: Variational Bayesian Variance Tracking.
 A. Godichon-Baggioni, N. Werge and O. Wintenberger Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data.
 G. Buritica, T. Mikosch and O. Wintenberger Large deviations of lp-blocks of regularly varying time series and applications to cluster inference.
 O. Wintenberger Stochastic Online Convex Optimization; Application to probabilistic time series forecasting.
 M. Oesting and O. Wintenberger Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors.
 N. Meyer, and O. Wintenberger Multivariate sparse clustering for extremes.
 E. Adjakossa, Y. Goude and O. Wintenberger. Kalman Recursions Aggregated Online.
Publications J.-M. Bardet, P. Doukhan and O. Wintenberger (2022) Contrast estimation of general locally stationary processes using coupling. Stoch. Proc. Appl., 152, 32-85.
 S. Mentemeier and O. Wintenberger (2022) Asymptotic Independence ex machina - Extreme Value Theory for the Diagonal Stochastic Recurrence Equation. J. Time Ser. Anal. 43, 750-780.
 G. Buritica, N. Meyer, T. Mikosch and O. Wintenberger (2021) Some variations on the extremal index, Zap. Nauchn. Sem. POMI 501, 52–77.
 J. de Vilmarest and O. Wintenberger (2021) Stochastic Online Optimization using Kalman Recursion, J. Mach. Learn. Res. 22, 1-55.
 N. Werge and O. Wintenberger (2022) AdaVol: An Adaptive Recursive Volatility Prediction Method, Econ. Stat. 23, 19-35.
 V. Margot, J.-P. Baudry, F. Guilloux and O. Wintenberger (2021) Consistent Regression using Data-Dependent Coverings, EJS 15, 1743-1782.
 N. Meyer and O. Wintenberger (2021) Sparse regular variation, Adv. Appl. Probab. 53, 1115-1148.
 C. Dombry, C. Tillier and O. Wintenberger (2022) Hidden regular variation for point processes and the single/multiple large point heuristic, Ann. Appl. Probab. 32, 191-234.
 N. Meyer and O. Wintenberger (2020) Discussion on "Graphical models for extremes" by Sebastian Engelke and Adrien Hitz, JRSS B.
 B. Basrak, O. Wintenberger and P. Zugec (2019) On total claim amount for marked Poisson cluster models, Adv. Appl. Probab., 51, 541-569.
 T. Mikosch, M. Rezapour and O. Wintenberger (2019) Heavy tails for an alternative stochastic perpetuity model, Stoch. Proc. Appl. 129, 4638-4662.
 O. Wintenberger (2018) Editorial: special issue on the extreme value analysis conference challenge “prediction of extremal precipitation”, Extremes, 21:425–429. The data and codes of the challenge are available here.
 P. Gaillard and O. Wintenberger (2018) Efficient online algorithms for fast-rate regret bounds under sparsity, Poster session of NeurIPS.
 V. Margot,J. P. Baudry, F. Guilloux and O. Wintenberger (2018). Rule Induction Partitioning Estimator. In International Conference on Machine Learning and Data Mining in Pattern Recognition (288-301). Springer.
 R. Kulik, P. Soulier and O. Wintenberger (2019) The tail empirical process of regularly varying functions of geometrically ergodic Markov chains, Stoch. Proc. Appl., 129, 4209-4238.
 R. S. Pedersen and O. Wintenberger (2018) On the tail behavior of a class of multivariate conditionally heteroskedastic processes, Extremes, 21:261–284.
 F. Blasques, P. Gorgi, S. J. Koopman and O. Wintenberger (2018) Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models EJS, 12, 1019-1052.
 N. Thiemann, C. Igel, O. Wintenberger, and Y. Seldin (2017) A strongly quasiconvex PAC-Bayesian bound. In Proceedings of Machine Learning Research, 76 (ALT).
 C. Tillier and O. Wintenberger (2017) Regular variation of a random length sequence of random variables and application to risk assessment, Extremes, 21, 27–56.
 P. Gaillard and O. Wintenberger (2017) Sparse Accelerated Exponential Weights, AISTAT, JMLR.
 O. Wintenberger (2017) Exponential inequalities for unbounded functions of geometrically ergodic Markov chains. Applications to quantitative error bounds for regenerative Metropolis algorithms, Statistics, 51.
 O. Wintenberger (2017) Optimal learning with Bernstein Online Aggregation, Machine Learning, 106. Erratum: The inequality (1) is incorrect in the unbounded case and the doubling trick should be uniform in j as in [CBMS07].
 C. Francq, O. Wintenberger and J.-M. Zakoïan (2018) Goodness-of-fit tests for extended Log-GARCH models and specification tests against the EGARCH, TEST 27, 27-51.
 T. Mikosch and O. Wintenberger (2016) A large deviations approach to limit theory for heavy-tailed time series, Probab. Th. Rel. Fields 166, 233-269.
 O. Wintenberger (2015) Weak transport inequalities and applications to exponential and oracle inequalities, EJP, 20, 114, 1-27.
 T. Mikosch and O. Wintenberger (2014) The cluster index of regularly varying sequences with applications to limit theory for functions of multivariate Markov chains, Probab. Th. Rel. Fields 159, 157-196.
 P. Alquier, X. Li and O. Wintenberger (2013) Prediction of time series by statistical learning: general losses and fast rates , Dependence Modeling, 1, 65-93. Note that this article contains the results of the unpublished working paper Fast Rates in Learning with Dependent Observations.
 C. Francq, O. Wintenberger and J.-M. Zakoïan (2013) GARCH models without positivity constraints: Exponential or Log GARCH?, Journal of Econometrics 177, 34-46.
 J. Trashorras and O. Wintenberger (2014) Large deviations for bootstrapped empirical measures, Bernoulli 20, 1845-1878.
 O. Wintenberger (2013) Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model, Scandinavian Journal of Statistics 40, 846-867.
 T. Mikosch and O. Wintenberger (2013) Precise large deviations for dependent regularly varying sequences, Probab. Th. Rel. Fields 156, 851-887.
 J.-M. Bardet, W. Kengne, and O. Wintenberger (2012) Detecting multiple change-points in general causal time series using penalized quasi-likelihood, Electron. J. Statist. 6, 435-477.
 P. Alquier, O. Wintenberger (2012) Model selection and randomization for weakly dependent time series forecasting, Bernoulli 18 (3), 883-913.
 K. Bartkiewicz, A. Jakubowski, T. Mikosch, O. Wintenberger (2011) Stable limits for sums of dependent infinite variance random variables Probab. Th. Rel. Fields 150, 337-372.
 O. Wintenberger (2010) Deviation inequalities for sums of weakly dependent time series, Elect. Comm. in Probab. 15, 489-503.
 I. Gannaz, O. Wintenberger (2010) Adaptive density estimation under weak dependence, ESAIM Probab. Statist. 14, 151-172.
 J.-M. Bardet, O. Wintenberger (2009) Asymptotic normality of the Quasi Maximum Likelihood Estimator for multidimensional causal processes, Ann. Statist. 37, 2730-2759.
 P. Doukhan, O. Wintenberger (2008) Weakly dependent chains with infinite memory, Stoch. Proc. Appl. 118, 11, 1997-2013.
 P. Doukhan, O. Wintenberger (2007) An invariance principle for weakly dependent stationary general models, Probab. Math. Statist. 27, 1, 45-73.
 N. Ragache, O.Wintenberger (2006) Convergence rates for density estimators of weakly dependent time series , Dependence in Probability and Statistics, (Eds P. Bertail, P. Doukhan and P. Soulier),Lecture Notes in Statist. 187, 349-372.
The manuscript of my habilitation is available here and my defence slides here.
The manuscript of my PHD is available here and my defence slides here.