1. High-dimensional variable clustering based on maxima of a weakly dependent random process
- Author
-
Boulin, Alexis, Di Bernardino, Elena, Laloë, Thomas, and Toulemonde, Gwladys
- Subjects
Mathematics - Statistics Theory ,Statistics - Methodology ,Statistics - Machine Learning ,60G70, 62H05, 62M99 - Abstract
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 depending on a tuning parameter that recovers 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. A data-driven selection method for the tuning parameter is also proposed. 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., Comment: 47 pages, 6 figures
- Published
- 2023