Year: 2024
Type: preprint
Abstract: We provide a review and a comparison of methods for differential network estimation in Gaussian graphical models with focus on structure learning. We consider the case of two datasets from distributions associated with two graphical models. In our simulations, we use five different methods to estimate differential networks. We vary graph structure and sparsity to explore their influence on performance in terms of power and false discovery rate. We demonstrate empirically that presence of hubs proves to be a challenge for all the methods, as well as increased density. We suggest local and global properties that are associated with this challenge. Direct estimation with lasso penalized D-trace loss is shown to perform the best across all combinations of network structure and sparsity.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4405783408