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Contribution to probabilistic and statistical modelling: stochastic models, statistical estimation, extremes and spatial models
Thèses et HDR   Open Access

Contribution to probabilistic and statistical modelling: stochastic models, statistical estimation, extremes and spatial models

Solym Manou-Abi
Habilitation à diriger des recherches, Université de Montpellier
05/07/2024

Résumé

Extreme statistics Markov chains Heavy-tail property Stable process Spatial processes Stochastic differential equation Machine Leanring Statistique des processus Statistique des extremes Equation Diffentielle Stochastique Processus stable Chaînes de Markov Apprentissage automatique Approximation
This habilitation deals with both theoretical and applied aspects of probability, mathematical statistics and computational statistics, including modelling for application to real data. The theoretical tools concerned are Markov chains, heavy-tailed laws and stable processes, spatial processes, stochastic differential equations with approaches to the existence of solutions, approximation and parametric or non-parametric estimation, and extreme value statistics. I am interested in mixing conditions, statistical inference, numerical approximation, spatial interpolation and machine learning. In terms of applications, I am interested in health-environment data (vector capture data for arboviroses and infectious diseases), epidemiology (Covid-19, Dengue fever, thyphoid fever, etc.), econometrics and quantitative finance (price variation).

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