Soutenance de thèse de Nikita GUSAROV (GROG)
Intitulée : "Performances des modèles économétriques et de Machine Learning pour l’étude économique des choix discrets de consommation"
Établissement : Université Grenoble Alpes
Ecole doctorale : SE - Sciences Economiques
Doctorat : Sciences économiques
Membres du jury :
Keywords: Data science, Preference studies, Artificial datasets, Econometrics, Machine learning, Consumer choice
Abstract :
This thesis is a cross-disciplinary study of discrete choice modeling, addressing both econometrics and machine learning (ML) techniques applied to individual choice modeling. The problematic arises from insufficient points of contact among users (economists and engineers) and data scientists, who pursue different objectives, although using similar techniques. To bridge this interdisciplinary gap, the PhD work proposes a unified framework for model performance analysis. It facilitates the comparison of data analysis techniques under varying assumptions and transformations. The designed framework is suitable for a variety of econometrics and ML models. It addresses the performance comparison task from the research procedure perspective, incorporating all the steps potentially affecting the performance perceptions. To demonstrate the framework’s capabilities we propose a series of 3 applied studies. In those studies the model performance is explored face to the changes in (1) sample size and balance, resulting from data collection; (2) changes in preferences structure within population, reflecting incorrect behavioral assumptions; and (3) model selection, directly intertwined with the performance perception.
La soutenance sera également diffusée via Zoom, le lien sera mis à jour dans les semaines à venir.
Ecole doctorale : SE - Sciences Economiques
Doctorat : Sciences économiques
Membres du jury :
- M. Iragael JOLY - Grenoble INP, Directeur de thèse
- M. André DE PALMA - CY Cergy Paris Université, Rapporteur
- Mme Maria BÖRJESSON - VTI Swedish Transport Research Institute, Rapporteure
- Mme Nadine MASSARD - Université Grenoble Alpes, Examinatrice
- M. Michel BIERLAIRE - École Polytechnique Fédérale de Lausanne, Examinateur
- M. Michel SIMIONI - INRAE, Examinateur
- M. Pierre LEMAIRE - Grenoble INP, Invité (Co-directeur de thèse)
Keywords: Data science, Preference studies, Artificial datasets, Econometrics, Machine learning, Consumer choice
Abstract :
This thesis is a cross-disciplinary study of discrete choice modeling, addressing both econometrics and machine learning (ML) techniques applied to individual choice modeling. The problematic arises from insufficient points of contact among users (economists and engineers) and data scientists, who pursue different objectives, although using similar techniques. To bridge this interdisciplinary gap, the PhD work proposes a unified framework for model performance analysis. It facilitates the comparison of data analysis techniques under varying assumptions and transformations. The designed framework is suitable for a variety of econometrics and ML models. It addresses the performance comparison task from the research procedure perspective, incorporating all the steps potentially affecting the performance perceptions. To demonstrate the framework’s capabilities we propose a series of 3 applied studies. In those studies the model performance is explored face to the changes in (1) sample size and balance, resulting from data collection; (2) changes in preferences structure within population, reflecting incorrect behavioral assumptions; and (3) model selection, directly intertwined with the performance perception.
La soutenance sera également diffusée via Zoom, le lien sera mis à jour dans les semaines à venir.
Infos date
Lundi 19 février 2024 à 14h00
Infos lieu
Amphi Barbillon (site Viallet)Grenoble INP UGA 46 avenue Félix Viallet 38000 Grenoble