Scalable bayesian bi-level variable selection in generalized linear models - Centre de Recherche en Économie et Statistique
Article Dans Une Revue Foundations of Data Science Année : 2024

Scalable bayesian bi-level variable selection in generalized linear models

Younès Youssfi
  • Fonction : Auteur

Résumé

Motivated by a real-world application in cardiology, this paper introduces a novel algorithm designed for Bayesian bi-level variable selection in generalized linear models. The methodology is specifically tailored to address the challenges posed by large datasets, characterized by both a substantial number of individuals and predictors. Our approach relies on the waste-free Sequential Monte Carlo (SMC) methodology developed by [3]. Notably, we address the intricate constraints inherent in bi-level variable selection, where the selection of an individual predictor is contingent upon the prior selection of its group. To tackle this challenge, we present a new proposal mechanism that effectively navigates these constraints, ensuring a robust and efficient variable selection process. Additionally, our algorithm incorporates the Approximate Laplace Approximation (ALA) approach proposed by [11], enhancing the computational efficiency of our method. We demonstrate in our numerical study that the algorithm consistently offers reliable performance on large datasets within a few minutes, on both simulated data and real-world data related to the aforementioned cardiology application.
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Dates et versions

hal-04793340 , version 1 (06-01-2025)

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Younès Youssfi, Nicolas Chopin. Scalable bayesian bi-level variable selection in generalized linear models. Foundations of Data Science, 2024, ⟨10.3934/fods.2024010⟩. ⟨hal-04793340⟩
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