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‘La amiga estupenda’ por Elena Ferrante.
‘Invisible women’ by Caroline Criado Perez.
Short description of portfolio item number 1
Short description of portfolio item number 2
Published in Zap. Nauchn. Semin. POMI. Volume 501, Probability and Statistics, 2021
This paper is an overview on the extremal index for stationary time series. It provides a new interpretation in terms of α-clusters, where α is the tail-index of the series.
Recommended citation: G. Buriticá, N. Meyer, T. Mikosch, O. Wintenberger. (2021). Some variations of the extremal index. Zap. Nauchn. Semin. POMI. Volume 501, Probability and Statistics.* **30**, 52—77. To be translated in J.Math.Sci. (Springer). https://arxiv.org/abs/2106.05117
Large deviations of lp-blocks of regularly varying time series and applications to cluster inference
Published in Submitted, 2022
This paper studies large deviations of p-norms of stationary regularly varying time series. It introduces α-clusters, where α is the tail-index of the series, and proposes consistent disjoint blocks estimators of α-cluster features. This new methodology proves to be robust to handle time dependencies.
Recommended citation: G. Buriticá, T. Mikosch, O. Wintenberger. (2022). Large deviations of lp-blocks of regularly varying time series and applications to cluster inference. https://arxiv.org/abs/2106.12822
Published in Environmetrics, 2022
This paper presents the stable sums method to infer high return levels of multivariate heavy-tailed time series. This new method is justified by the large deviations of powers of α-sums, where α is the tail-index of the series. Its main advantage is that is implementation coincides for dependent and independent time series.
Recommended citation: G. Buriticá, P. Naveau. (2022). Stable sums to infer high return levels of multivariate rainfall time series, Environmetrics, e2782. https://doi.org/10.1002/env.2782
Published in Submitted, 2022
Recently, we proposed a new estimator for cluster inference for heavy-tailed time series. This paper states the asymptotic normality of our α-cluster-based estimator. We infer the extremal index, the cluster lengths and other important indices in extremes. Also, we compute the asymptotic variances for ARMA models and stochastic recurrence equations, which shows our estimator compares favourably with classical approaches in terms of variance.
Recommended citation: G. Buriticá, O. Wintenberger. (2022). On the asymptotics of extremal lp-blocks cluster inference. https://arxiv.org/abs/2212.13521
CIRM on New Results on Time Series and their Statistical Applications
9th Young Statisticians and Probabilists on Extreme Values
Valpred workshop on validation of forecasting and relative topics
Graduate course M1, Sorbonne Université, Mathématiques et applications, 2019
Code assignments for the course taught by [Maud Thomas] in the Fall of 2019 and 2020.
Graduate course M1, Sorbonne université, statistiques, 2019
Graduate course M1, ISUP, actuarial sciences, 2019
Exercises assignments for the course on Time series taught by [Olivier Wintenberger] in the Fall 2019, 2020 and 2021.
Undergraduate course L3, AgroParisTech, 2020
Introductory course to probability and statistics for engineering school AgroParisTech I taught in 2020 and 2021.