Network Inference for non-Gaussian Data

Music City Center 

Networks provide a powerful framework for capturing complex interactions among variables and analyzing their unified functions. In this study, we propose a novel method for inferring undirected networks in parametric models, specifically designed for non-Gaussian data. Our approach assumes a flexible distribution family for each variable that accommodates heavy tails and skewness, which are two common data features leading to deviations from normality. The method constructs an undirected network by sequentially inferring both network structure and edge strength. The network structure is estimated based on Gaussian-transformed data, adapting the non-paranormal framework by integrating parametric statistics to improve stability and computational efficiency. Edge strengths within the estimated network are subsequently evaluated by quantifying the conditional independence to incorporate parametric statistics through the assumed distribution family, facilitating efficient and precise calculations. By addressing challenges in modeling complex data, our method offers enhanced flexibility and provides new insights for non-Gaussian network construction.

Conditional independence

Network inference

Non-Gaussian data

Nonparanormal transformation

Parametric statistics

Undirected networks 

Section on Statistical Learning and Data Science