# Research

** Preprints**

[49] P Maillard and O Wintenberger Moment conditions for random coefficient AR(∞) under non-negativity assumptions.

[48] A. Godichon-Baggioni, N. Werge and O. Wintenberger Learning from time-dependent streaming data with online stochastic algorithms.

[47] J. de Vilmarest and O. Wintenberger Viking: Variational Bayesian Variance Tracking.

[46] A. Godichon-Baggioni, N. Werge and O. Wintenberger Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Streaming Data.

[45] G. Buritica, T. Mikosch and O. Wintenberger Large deviations of lp-blocks of regularly varying time series and applications to cluster inference.

[44] O. Wintenberger Stochastic Online Convex Optimization; Application to probabilistic time series forecasting.

[43] M. Oesting and O. Wintenberger Estimation of the Spectral Measure from Convex Combinations of Regularly Varying Random Vectors.

[42] N. Meyer, and O. Wintenberger Multivariate sparse clustering for extremes.

[41] E. Adjakossa, Y. Goude and O. Wintenberger. Kalman Recursions Aggregated Online.

** Publications**

[39] 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.

[38] G. Buritica, N. Meyer, T. Mikosch and O. Wintenberger (2021) Some variations on the extremal index, Zap. Nauchn. Sem. POMI 501, 52–77.

[37] J. de Vilmarest and O. Wintenberger (2021) Stochastic Online Optimization using Kalman Recursion, J. Mach. Learn. Res. 22, 1-55.

[36] N. Werge and O. Wintenberger (2022) AdaVol: An Adaptive Recursive Volatility Prediction Method, Econ. Stat. 23, 19-35.

[35] V. Margot, J.-P. Baudry, F. Guilloux and O. Wintenberger (2021) Consistent Regression using Data-Dependent Coverings, EJS 15, 1743-1782.

[34] N. Meyer and O. Wintenberger (2021) Sparse regular variation, Adv. Appl. Probab. 53, 1115-1148.

[33] 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.

[32] N. Meyer and O. Wintenberger (2020) Discussion on "Graphical models for extremes" by Sebastian Engelke and Adrien Hitz, JRSS B.

[31] B. Basrak, O. Wintenberger and P. Zugec (2019) On total claim amount for marked Poisson cluster models, Adv. Appl. Probab., 51, 541-569.

[30] T. Mikosch, M. Rezapour and O. Wintenberger (2019) Heavy tails for an alternative stochastic perpetuity model, Stoch. Proc. Appl. 129, 4638-4662.

[29] 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.

[28] P. Gaillard and O. Wintenberger (2018) Efficient online algorithms for fast-rate regret bounds under sparsity, Poster session of NeurIPS.

[27] 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.

[26] 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.

[25] R. S. Pedersen and O. Wintenberger (2018) On the tail behavior of a class of multivariate conditionally heteroskedastic processes, Extremes, 21:261–284.

[24] 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.

[23] N. Thiemann, C. Igel, O. Wintenberger, and Y. Seldin (2017) A strongly quasiconvex PAC-Bayesian bound. In Proceedings of Machine Learning Research, 76 (ALT).

[22] 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.

[21] P. Gaillard and O. Wintenberger (2017) Sparse Accelerated Exponential Weights, AISTAT, JMLR.

[20] O. Wintenberger (2017) Exponential inequalities for unbounded functions of geometrically ergodic Markov chains. Applications to quantitative error bounds for regenerative Metropolis algorithms, Statistics, 51.

[19] 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].

[18] 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.

[17] 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.

[16] O. Wintenberger (2015) Weak transport inequalities and applications to exponential and oracle inequalities, EJP, 20, 114, 1-27.

[15] 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.

[14] 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.

[13] C. Francq, O. Wintenberger and J.-M. Zakoïan (2013) GARCH models without positivity constraints: Exponential or Log GARCH?, Journal of Econometrics 177, 34-46.

[12] J. Trashorras and O. Wintenberger (2014) Large deviations for bootstrapped empirical measures, Bernoulli 20, 1845-1878.

[11] O. Wintenberger (2013) Continuous Invertibility and Stable QML Estimation of the EGARCH(1,1) Model, Scandinavian Journal of Statistics 40, 846-867.

[10] T. Mikosch and O. Wintenberger (2013) Precise large deviations for dependent regularly varying sequences, Probab. Th. Rel. Fields 156, 851-887.

[9] 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.

[8] P. Alquier, O. Wintenberger (2012) Model selection and randomization for weakly dependent time series forecasting, Bernoulli 18 (3), 883-913.

[7] 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.

[6] O. Wintenberger (2010) Deviation inequalities for sums of weakly dependent time series, Elect. Comm. in Probab. 15, 489-503.

[5] I. Gannaz, O. Wintenberger (2010) Adaptive density estimation under weak dependence, ESAIM Probab. Statist. 14, 151-172.

[4] J.-M. Bardet, O. Wintenberger (2009) Asymptotic normality of the Quasi Maximum Likelihood Estimator for multidimensional causal processes, Ann. Statist. 37, 2730-2759.

[3] P. Doukhan, O. Wintenberger (2008) Weakly dependent chains with infinite memory, Stoch. Proc. Appl. 118, 11, 1997-2013.

[2] P. Doukhan, O. Wintenberger (2007) An invariance principle for weakly dependent stationary general models, Probab. Math. Statist. 27, 1, 45-73.

[1] 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.