2019-2020

À moins d’indication contraire, tous les séminaires de STATQAM ont lieu à 15h30 au Pavillon Président-Kennedy (PK), PK-5115, 201, avenue du Président-Kennedy, Montréal (QC) H2X 2J6.

Session Automne 2019

Jeudi 5 septembre : Paul Doukhan (Univ. Cergy, France)
Titre : Non stationnarité et applications

Résumé : La stationnarité est une hypothèse courante en statistique des séries temporelles. Celle ci est raisonnable lorsque la dynamique de l’évolution d’un phénomène ne change pas au cours du temps. De fait la condition utile est l’ergodicité car elle conduit à des lois de grands nombres justifiant la consistance d’estimations naturelles. En réalité les dynamiques ne sont souvent pas homogènes dans le temps. L’objectif de l’exposé est de proposer des conditions adaptées à des phénomènes réels. Outre les ruptures de régimes.m, les conditions de stationnarité locales introduites par Dahlhaus semblent une approche adaptée. Nous essaierons de dégager des modèles (par exemple des chaînes de Markov ou des modèles à mémoire plus généraux), des techniques (des propriétés de dépendance, ou des inégalités spécifiques) ainsi que des applications (en apprentissage statistique, en astronomie, météorologie, ou adaptées à la vente en ligne) à ce cadre très général.

Jeudi 12 septembre : Janosch Ortmann (ESG, UQAM)
Title: KPZ universality: last passage percolation, polymers and particles

Abstract: KPZ universality describes a scaling behaviour that differs from the central limit theorem by the size of the fluctuations ($n^{1/3}$ instead of $n^{1/2}$) and the limiting distribution. Instead of the Gaussian, the Tracy-Widom distributions from random matrix theory appear in the limit. It is a long standing conjecture that the KPZ universality class contains a large group of models, including particle systems, last-passage and polymer models. Beyond its physical motivation, the study of KPZ universality involves a surprising range of mathematical tools, including algebra, combinatorics, analysis and stochastic calculus. In this talk, I will give an overview of the KPZ universality class and discuss some specific models, based on joint work with Duncan Dauvergne, Nicos Georgiou, Neil O’Connell, Jeremy Quastel, Daniel Remenik and Bálint Virág.

Jeudi 19 septembre : Paquito Bernard (Sc. de l'activité physique, UQAM)
Titre: Un modèle additif généralisé, pourquoi est-ce utile en sciences de l’activité physique ?

Résumé: La présentation permettra de décrire les caractéristiques et l’utilité d’un modèle additif généralisé (MAG). L’intervenant expliquera en quoi le MAG lui permet de répondre à de nouvelles questions de recherche et partagera quelques applications, notamment sur la modélisation des patrons d’activité physique avec différents indicateurs de santé.

Jeudi 26 septembre : Francisco Cuevas Pacheco (UQAM)
Title: A family of covariance functions for random fields on spheres
Abstract: The Matérn family of isotropic covariance functions has been central to the theoretical development and application of statistical models for geospatial data. For global data defined over the whole sphere representing planet Earth, the natural distance between any two locations is the great circle distance. In this setting, the Matern family of covariance functions has a restriction on the smoothness parameter, making it an unappealing choice to model smooth data. Finding a suitable analogue for modelling data on the sphere is still an open problem. This work proposes a new family of isotropic covariance functions for random fields defined over the sphere. The proposed family has four parameters, one of which indexes the mean square differentiability of the corresponding Gaussian field, and also allows for any admissible range of fractal dimension. We apply the proposed model to a dataset of precipitable water content over a large portion of the Earth, and show that the model gives more precise predictions of the underlying process at unsampled locations than does the Matérn model using chordal distances.
Vendredi 4 octobre : journée d'automne de STATLAB
– 14h Linda Mhalla (Gerad)
– 14h45 Florian Maire (UdM)
– 16h Johanna Nešlehová (McGill, prix CRM-SSC)
Inscription gratuite (mais obligatoire pour les deux premiers exposés)
http://www.crm.umontreal.ca/2019/StatLabMethods19/
Jeudi 10 octobre : Sebastian Engelke (Univ. Genève, Suisse)
Title: Causal discovery in heavy-tailed models
Abstract: Causal questions are omnipresent in many scientific problems. While much progress has been made in the analysis of causal relationships between random variables, these methods are not well suited if the causal mechanisms manifest themselves only in extremes. This work aims to connect the two fields of causal inference and extreme value theory. We define the causal tail coefficient that captures asymmetries in the extremal dependence of two random variables. In the population case, the causal tail coefficient is shown to reveal the causal structure if the distribution follows a linear structural causal model. This holds even in the presence of latent common causes that have the same tail index as the observed variables. Based on a consistent estimator of the causal tail coefficient, we propose a computationally highly efficient algorithm that infers causal structure from finitely many data. We prove that our method consistently estimates the causal order and compare it to other well-established and non-extremal approaches in causal discovery on synthetic data. This is joint work with Nicola Gnecco, Nicolai Meinshausen and Jonas Peters; preprint available on https://arxiv.org/abs/1908.05097
Jeudi 17 octobre : Gabor Lugosi (Pompeu Fabra University, Spain)
Title: Network archeology: on revealing the past of random trees
Abstract: Networks are often naturally modeled by random processes in which nodes of the network are added one-by-one, according to some random rule. Uniform and preferential attachment trees are among the simplest examples of such dynamically growing networks. The statistical problems we address in this talk regard discovering the past of the network when a present-day snapshot is observed. Such problems are sometimes termed « network archeology ». We present a few results that show that, even in gigantic networks, a lot of information is preserved from the very early days.
Jeudi 24 octobre : Denis Larocque (HEC Montréal)
Title: Prediction Intervals with Random Forests
Abstract: The classical and most commonly used approach to building prediction intervals is the parametric approach. However, its main drawback is that its validity and performance highly depend on the assumed functional link between the covariates and the response. This research investigates new methods that improve the performance of prediction intervals with random forests. Two aspects are explored: The method used to build the forest and the method used to build the prediction interval. Four methods to build the forest are investigated, three from the CART paradigm and the transformation forest method. For CART forests, in addition to the default least squares splitting rule, two alternative splitting criteria are investigated. We also present and evaluate the performance of five flexible methods for constructing prediction intervals. This yields 20 distinct method variations. To reliably attain the desired confidence level, we include a calibration procedure performed on the out-of-bag information provided by the forest. The 20 method variations are thoroughly investigated and compared to five alternative methods through simulation studies and in real data settings. The results show that the proposed methods are very competitive. They outperform commonly used methods in both in simulation settings and with real data. This is joint work with Marie-Hélène Roy.
Vendredi 1er novembre (colloque ISM-CRM, Burnside Hall room 1104, McGill Univ., 16:00-17:00): Stephen Walker
Title: General Bayesian modeling
Abstract : The work is motivated by the inflexibility of Bayesian modeling; in that only parameters of probability models are required to be connected with data. The idea is to generalize this by allowing arbitrary unknowns to be connected with data via loss functions. An updating process is then detailed which can be viewed as arising in at least a couple of ways – one being purely axiomatically driven. The further exploration of replacing probability model based approaches to inference with loss functions is ongoing. Joint work with Chris Holmes, Pier Giovanni Bissiri and Simon Lyddon.
Jeudi 7 novembre : Tibor Schuster (McGill)
TBA
Jeudi 14 novembre : Léo Belzile (HEC, Montréal)
Espérance de vie humaine à des âges extrêmes
Vendredi 22 novembre (colloque des sciences mathématiques, 16h00, PK-5115): Don Estep (SFU, CANSSI Director)
Title: Formulation and solution of stochastic inverse problems for science and engineering models
Abstract: The stochastic inverse problem of determining probability structures on input parameters for a physics model corresponding to a given probability structure on the output of the model forms the core of scientific inference and engineering design. We describe a formulation and solution method for stochastic inverse problems that is based on functional analysis, differential geometry, and probability/measure theory. This approach yields a computationally tractable problem while avoiding alterations of the model like regularization and ad hoc assumptions about the probability structures. We present several examples, including a high-dimensional application to determination of parameter fields in storm surge models. We also describe work aimed at defining a notion of condition for stochastic inverse problems and tackling the related problem of designing sets of optimal observable quantities.
Jeudi 28 novembre : Jonathan Jalbert (Poly Montréal)
TBA

Session Hiver 2020

Jeudi 9 janvier : TBA
TBA
Jeudi 16 janvier : Eric Marchand (Univ. Sherbrooke)
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Jeudi 23 janvier
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Vendredi 31 janvier (colloque CRM-ISM) : Ana-Maria Staicu (NCSU)
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Jeudi 6 février :
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Jeudi 13 février :
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Jeudi 20 février
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Vendredi 28 février (colloque CRM-ISM, Concordia):
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Jeudi 5 mars
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Jeudi 12 mars
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Jeudi 19 mars
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Jeudi 26 mars
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Vendredi 3 avril (colloque CRM-ISM, Université de Montréal)
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Jeudi 16 avril
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