Work

G{é}n{é}ration de Matrices de Corr{é}lation avec des Structures de Graphe par Optimisation Convexe

Year: 2025

Type: preprint

Abstract: This work deals with the generation of theoretical correlation matrices with specific sparsity patterns, associated to graph structures. We present a novel approach based on convex optimization, offering greater flexibility compared to existing techniques, notably by controlling the mean of the entry distribution in the generated correlation matrices. This allows for the generation of correlation matrices that better represent realistic data and can be used to benchmark statistical methods for graph inference.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2503.21298

Authors: Fahkar, Ali, Polisano, Kévin, Gannaz, Irène, Achard, Sophie

Institutions: STATIFY: Modèles statistiques bayésiens et des valeurs extrêmes pour données structurées et de grande dimension, Adventist Health Simi Valley, Laboratoire des Sciences pour la Conception, l'Optimisation et la Production

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4415062397

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