- Preprints  T. Mikosch, M. Rezapour and O. Wintenberger Heavy tails for an alternative stochastic perpetuity model.
- Publications  P. Gaillard and O. Wintenberger (2017) Sparse Accelerated Exponential Weights, accepted for AISTAT 2017, JMLR.
 F. Blasques, P. Gorgi, S. J. Koopman and O. Wintenberger Feasible Invertibility Conditions for Maximum Likelihood Estimation for Observation-Driven Models.
 C. Tillier and O. Wintenberger Regular variation of a random length sequence of random variables and application to risk assessment.
 R. S. Pedersen and O. Wintenberger On the tail behavior of a class of multivariate conditionally heteroskedastic processes.
 R. Kulik, P. Soulier and O. Wintenberger The tail empirical process of regularly varying functions of geometrically ergodic Markov chains.
 O. Wintenberger (2016) Exponential inequalities for unbounded functions of geometrically ergodic Markov chains. Applications to quantitative error bounds for regenerative Metropolis algorithms, Statistics, Special Issue in honor of Paul Doukhan, Online first.
 O. Wintenberger (2016) Optimal learning with Bernstein Online Aggregation, Machine Learning, Online first. Erratum: The inequality (1) is wrong in the unbounded case and the doubling trick should be uniform in j as in [CBMS07].
 C. Francq, O. Wintenberger and J.-M. Zakoïan (2016) Goodness-of-fit tests for extended Log-GARCH models and specification tests against the EGARCH, TEST, Online first.
 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 Note that this article contains the results of the unpublished working paper Fast Rates in Learning with Dependent Observations.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 (2013) Large deviations for bootstrapped empirical measures, accepted for publication in Bernoulli.
 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.