BEGIN:VCALENDAR PRODID:-//eluceo/ical//2.0/EN VERSION:2.0 CALSCALE:GREGORIAN BEGIN:VEVENT UID:6aab93bd4e4df10905139bb1e2cafbff DTSTAMP:20260825T222704Z SUMMARY:LIDAM Statistics Seminar by Kayvan Sadeghi DESCRIPTION:25/09/2026 - 14:30 - ISBA C115 - \; \;Kayvan Sadeghi &n bsp\;(University College London) \;Will give a presentation on : \ ;Characterising \;and Identifying Graphical Causal ModelsAbstract:&nbs p\;Using a characterization of faithfulness\, we describe the foundational graph orientation rule in constraint-based causal structure learning and the assumptions under which it recovers the “correct” graph. We presen t the theory for the class of directed acyclic graphs (DAGs) and then gene ralize it to the broader class of mixed graphs\, which can encode independ ence structures arising from feedback\, latent variables\, and selection m echanisms. We also introduce a general and computationally efficient struc ture learning algorithm for mixed graphs. URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20260925T143000 DTEND;TZID=Europe/Brussels:20260925T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:866ac6bf7e30bb37158f338bfb2157b2 DTSTAMP:20260825T222704Z SUMMARY:Applied Statistics Workshop DESCRIPTION:More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261002T143000 DTEND;TZID=Europe/Brussels:20261002T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:bb1d56a09f97140c5d27fa25607b181a DTSTAMP:20260825T222704Z SUMMARY:Applied Statistics Workshop by Marina Vives DESCRIPTION:More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261106T143000 DTEND;TZID=Europe/Brussels:20261106T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:e1388db9f322ad608b1ffe8ad0304c87 DTSTAMP:20260825T222704Z SUMMARY:LIDAM Statistics Seminar by Ivan Kojadinovic DESCRIPTION:More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261127T110000 DTEND;TZID=Europe/Brussels:20261127T120000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:7fbfce2c1c0e1d35c6e594013412e57b DTSTAMP:20260825T222704Z SUMMARY:Applied Statistics Workshop by Aurelie Bertrand and Laura Symul DESCRIPTION:More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261127T143000 DTEND;TZID=Europe/Brussels:20261127T153000 LOCATION:ISBA - C115 (1st Floor) 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:9f8a838c6c984dce4d5c83a831a40cb8 DTSTAMP:20260825T222704Z SUMMARY:LIDAM Statistics Seminar by Alessandra Luati DESCRIPTION:11/12/2026 - 14:30 - ISBA C115 - \; \;Alessandra Luati  \;(Imperial College London) \;More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261211T143000 DTEND;TZID=Europe/Brussels:20261211T153000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:8b7818f10d55acb1a506094dbab82e30 DTSTAMP:20260825T222704Z SUMMARY:ISBA Young Researchers Day / YRD DESCRIPTION:18/09/2026 - 09:00 - ISBA C115 - \; \;9h00  \;Intro duction9h10  \;Tommaso Martini"Smoothing Copulas Without Smoothing Awa y Tail Dependence"Abstract:Smooth nonparametric copula estimators have bee n found to improve finite-sample performance over the empirical copula in a range of settings. A prominent example is the empirical beta copula intr oduced by Segers\, Sibuya\, and Tsukahara. At any fixed sample size\, howe ver\, the empirical beta copula has zero lower- and upper-tail dependence coefficients. This motivates the study of smoothing mechanisms that retain the benefits of smoothing while permitting nontrivial tail dependence. Th e empirical beta copula and\, more generally\, the class of smooth nonpara metric copula estimators studied by Kojadinovic and Yi admit a common plug -in representation through an operator that averages a starting copula wit h respect to a family of smoothing distributions. We study the tail behavi our of the copula produced by this operator. Under suitable regularity con ditions\, we derive an explicit expression for its lower tail copula. The expression shows how the induced lower-tail behaviour is shaped jointly by the starting copula\, by the marginal behaviour of the smoothing distribu tions\, and by the lower tail copula of the common survival copula governi ng their dependence structure. For grid-supported smoothing distributions\ , the general result reduces to finite-sum expressions for the lower tail copula and its associated lower-tail dependence coefficients\, extending c omposite Bernstein-type constructions beyond binomial smoothing. These res ults also suggest a class of rank-based semiparametric copula estimators i n which a nonparametric pilot provides the starting dependence structure\, while a parametric survival copula enters through the smoothing mechanism and influences the induced lower-tail behaviour of the smoothed estimator .9h50  \;Cyril Ghislain"Distribution-Free Runs Tests for Directional S erial Dependence via Measure Transportation"Abstract:Directional runs stat istics provide natural measures of serial dependence\, but their null dist ribution generally depends on the unknown marginal distribution of the obs ervations. This prevents the direct extension of the distribution-free Wal d–Wolfowitz test to multivariate directional data. We address this probl em by transporting directional signs to a uniform distribution on the sphe re before measuring their serial alignment. The resulting empirical proced ure is based on an optimal assignment of the observations to a determinist ic spherical grid. We establish the asymptotic normality of the resulting statistic and show that estimating the underlying direction is asymptotica lly costless. Under contiguous Markov alternatives generating weak serial alignment\, the lag-one statistic coincides with the central sequence of t he limiting experiment and yields a locally asymptotically most powerful t est. Simulations support the finite-sample accuracy of the asymptotic theo ry.10h20  \;Lise Léonard \;"Asymptotic Inference for High-Dimensi onal Linear Regression via Model Averaging Debiased SLOPE"Abstract:We cons ider the problem of estimation and inference in high-dimensional linear re gression models where the number of covariates exceeds the sample size. Wh ile penalized estimators such as the Lasso provide effective variable sele ction\, their use for statistical inference is hindered by two key limitat ions: they are sparse and depend on a regularization parameter that is unk nown in practice.The SLOPE estimator\, recently proposed in the literature \, is a sorted $\\ell_1$-penalized regression method that assigns larger p enalties to larger coefficients. It can be viewed as a generalization of t he Lasso and enjoys improved oracle properties\, including minimax-optimal estimation rates. \;Leveraging these advantages\, we address both cha llenges simultaneously by proposing MADSlope\, which combines a debiasing procedure with model averaging over a grid of regularization parameters.We establish that MADSlope is asymptotically Gaussian\, even when the averag ing weights are data-driven and thus random\, and we prove that the weight ing scheme is asymptotically optimal in terms of prediction loss. Moreover \, the resulting estimator is tuning-free\, eliminating the need to select a single regularization parameter.Our theoretical guarantees are supporte d by simulation studies and an application to a real high-dimensional data set.10h50  \;Break11h10  \;Philippe Hauchamps"Deciphering microbia l subcommunities of the vaginal microbiota: a statistical modelling approa ch using topic models"Abstract:The vaginal microbiota plays a crucial role in women’s health\, with its composition and dynamics influencing susce ptibility to infections\, adverse pregnancy outcomes\, and overall well-be ing. While mainstream approaches to characterize metagenomic sample compos ition have focused on dominant taxa\, some recent studies highlight the im portance of microbial subcommunities and their interactions within this co mplex ecosystem. \;In this talk\, we explore the use of statistical to pic models as a framework to decipher microbial subcommunities in the vagi nal microbiota. Topic models\, widely used in text mining\, allow for the identification of latent structures within high-dimensional count data. By applying this approach\, we aim to provide a data-driven characterization of bacterial subcommunities\, with the hope to shed some light on their e cological roles\, and their association with health and disease states.11h 50  \;Kenrick So"Climate Driven Mortality Forecasting using Deep Learn ing"Abstract:Climate extremes are now important drivers of mortality\, pro ducing sudden spikes that traditional models fail to predict. Despite this \, most mortality models ignore climate causing life insurers to be expose d to these extreme events. To address this\, we propose a two-step framewo rk that combines a regional weekly Lee–Carter baseline with CNN-LSTM and GNN-LSTM models to capture residual climate-mortality patterns. The CNN a nd GNN components capture spatial effects across regions\, while the LSTM models both short- and long-term temporal relationships of climate and mor tality. This allows our models to capture the response of mortality to the delayed and nonlinear climate effects. We demonstrate that our model capt ures the association between extreme temperatures and excess mortality\, a nd generates forecasts that account for both extreme events and forecast u ncertainty. As a result\, our proposed models are more accurate compared t o both the Lee–Carter baseline and a gated recurrent unit-based mortalit y model. From a risk management perspective\, the framework provides a mor e realistic assessment of extreme climate-driven mortality risk\, with imp ortant implications for Solvency II capital adequacy evaluation.12h20 &nbs p\;Brief ILV on-the-spot feedback and closing12h30  \;Lunch in the caf eteria URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20260918T090000 DTEND;TZID=Europe/Brussels:20260918T123000 LOCATION: Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:b84ccd605e85992dd887143f250963c2 DTSTAMP:20260825T222704Z SUMMARY:LIDAM Statistics Seminar by Kellie Archer DESCRIPTION:13/11/2026 - 14:30 -  \; \;Kellie Archer \;(The Ohi o State University) \;More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261113T143000 DTEND;TZID=Europe/Brussels:20261113T153000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:92a6eeda6d251cad51d46da7a5c30b74 DTSTAMP:20260825T222704Z SUMMARY: EDT short course by Margaux Zaffran DESCRIPTION:29-30/10/2026 - 09:30 -  \; \;Margaux Zaffran \;(Un iversité Paris-Saclay) \;More details coming soon->\; Please regist er at : Workshop EDT on Conformal Prediction – Remplir le formulaire URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261029T093000 DTEND;TZID=Europe/Brussels:20261030T150000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT BEGIN:VEVENT UID:140dce7cf811236038beced961ced323 DTSTAMP:20260825T222704Z SUMMARY:LIDAM Statistics Seminar by Thomas Jaki DESCRIPTION:16/10/2026 - 14:30 - ISBA C115 - \; \;Thomas Jaki   \;(Cambridge University) \;More details coming soon URL:https://uclouvain.be/en/calendar/isba DTSTART;TZID=Europe/Brussels:20261016T143000 DTEND;TZID=Europe/Brussels:20261016T153000 LOCATION:Voie du Roman Pays\, 20 1348 Louvain-la-Neuve END:VEVENT END:VCALENDAR