High-dimensional variable clustering based on sub-asymptotic maxima of a weakly dependent random process - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

High-dimensional variable clustering based on sub-asymptotic maxima of a weakly dependent random process

Résumé

We propose a new class of models for variable clustering called Asymptotic Independent block (AI-block) models, which defines population-level clusters based on the independence of the maxima of a multivariate stationary mixing random process among clusters. This class of models is identifiable, meaning that there exists a maximal element with a partial order between partitions, allowing for statistical inference. We also present an algorithm for recovering the clusters of variables without specifying the number of clusters \emph{a priori}. Our work provides some theoretical insights into the consistency of our algorithm, demonstrating that under certain conditions it can effectively identify clusters in the data with a computational complexity that is polynomial in the dimension. This implies that groups can be learned nonparametrically in which block maxima of a dependent process are only sub-asymptotic. To further illustrate the significance of our work, we applied our method to neuroscience and environmental real-datasets. These applications highlight the potential and versatility of the proposed approach.
Fichier principal
Vignette du fichier
2302.00934.pdf (1.77 Mo) Télécharger le fichier
Origine Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04397145 , version 1 (23-01-2024)

Licence

Identifiants

  • HAL Id : hal-04397145 , version 1

Citer

Alexis Boulin, Elena Di Bernardino, Thomas Laloë, Gwladys Toulemonde. High-dimensional variable clustering based on sub-asymptotic maxima of a weakly dependent random process. ICSDS 2023 - IMS International Conference on Statistics and Data Science, Dec 2023, Lisbon, Portugal. ⟨hal-04397145⟩
46 Consultations
13 Téléchargements

Partager

Gmail Mastodon Facebook X LinkedIn More