Probabilities and Statistic seminar

In this work, the joint parametric estimation of the drift coefficient, the scale coefficient, and the jump activity index in stochastic differential equations driven by a symmetric stable Lévy process is considered based on high-frequency observations. Firstly, the LAMN property for the corresponding Euler-type scheme is proven, and lower bounds for the estimation risk in this setting are deduced. Therefore, when the approximation scheme experiment is asymptotically equivalent to the high-frequency observation of the solution of the considered stochastic differential equation, these bounds can be transferred. Secondly, since the maximum likelihood estimator can be time-consuming for large samples, an alternative Le Cam’s one-step procedure is proposed in the general setting. It is based on an initial guess estimator, which is a combination of generalized variations of the trajectory for the scale and the jump activity index parameters, and a maximum likelihood type estimator for the drift parameter. This proposed one-step procedure is shown to be fast, asymptotically normal, and even asymptotically efficient when the scale coefficient is constant. In addition, the performances in terms of asymptotic variance and computation time on samples of finite size are illustrated with simulations. This talk is based on joint work with Alexandre Brouste and Laurent Denis.

(Exposé en français.)

Le calcul des rapports de constante de normalisation joue un rôle important dans la modélisation statistique. Deux exemples notables sont les tests d’hypothèses dans les modèles à variables latentes et la comparaison de modèles en statistique bayésienne. Dans ces deux cas, le rapport de vraisemblance et le facteur de Bayes sont définis comme le rapport des constantes de normalisation des distributions a posteriori. Nous proposons dans cet article une nouvelle méthodologie qui estime ce rapport en utilisant le principe de l’approximation stochastique. Notre estimateur est consistant et asymptotiquement gaussien. Sa variance asymptotique est plus faible que celle de l’estimateur populaire bridge sampling. En outre, il est beaucoup plus robuste lorsque les supports des deux distributions non normalisées considérées se chevauchent peu. Grâce à sa définition en ligne, notre procédure peut être intégrée dans un processus d’estimation dans les modèles à variables latentes, ce qui permet ainsi de réduire l’effort de calcul. Les performances de l’estimateur sont illustrées par une étude de simulation et comparées à celles de deux autres estimateurs : le ratio importance sampling et le bridge sampling.

Dans cet exposé, je vais décrire comment étudier un modèle probabiliste sur le groupe unitaire U(N), motivé par la théorie de Yang–Mills, à l’aide de la théorie des représentations. Plus précisément, nous verrons que la fonction de partition du modèle admet un développement asymptotique dont les coefficients sont liés aux nombres de Hurwitz, et la démonstration passe par un couplage de partitions aléatoires q-uniformes. Travail en collaboration avec Mylène Maïda (Université de Lille).

(Exceptionnellement, le séminaire aura lieu à Metz et sera diffusé en visio en salle de conférence à Nancy.)

In this talk, we discuss the question of Gibbs point processes in R^d with pairwise interactions that are not integrable at infinity. A standard example is the Riesz potential of the form g(x)=1/|x|^s where s<d. This setting has a long history, notably because the case s=d-2 corresponds to the classical Coulomb potential, which arises from electrostatic theory. We will first address the existence of the process in the infinite volume regime when a neutralizing background is introduced (this model is known as Jellium in theoretical physics). Subsequently, we will discuss the rigidity of such point processes, specifically hyper-uniformity and number rigidity. We will provide a state-of-the-art review and present numerous conjectures and open problems.

We define a wide class of Markovian load balancing queueing networks, including classic networks studied in the lively literature on the subject. Each network has identical single-server infinite-buffer queues and implements a load balancing policy to allocate each task at its arrival and possibly reallocate it at service completions. The purpose of the policy is to optimize server utilization under constraints such as limited information, real-time decision taking, and network topology. The queue length process is not necessarily exchangeable. The invariant law is in general not known even up to normalizing constant. We provide perfect simulation methods in view of Monte Carlo estimation of quantities of interest in equilibrium, for instance for performance evaluation. In this infinite multi-dimensional state space, we use an unusual preorder defining an order up to permutation of the coordinates, define a coupling in which networks in this class are dominated by the network with uniform routing, and implement dominated coupling from the past methods.

[The talk will be in French, but slides will be in English.]

Planning, titres et résumés ici.

Chemical reactions network inside cells have been extensively studied in order to better understand various biological phenomena. The majority of experimental studies are performed with cells that are part of a growing population. This population context is rarely taken into account even if selection between cells (due for example to growth) takes places within the studied system.
In this talk, I will represent such systems as continuous-time Markov chains. The measure-valued Markov process of the cell population will take into account the chemical reactions inside the cells as well as reactions between cells. By conditioning on non-absorption, we derive an equation for the expected population distribution within a growing population.
This extension of the Chemical Master Equation provides us a new framework to study cell population dynamics. I will present theoretical results on long-term behaviour of the population (stationary distribution, growth rate of the population) and an application of this framework to experimental data.