Tests for differential Gaussian Bayesian networks based on quadratic inference functions
Abstract
Hypotheses testing procedures based on quadratic inference functions are proposed to test whether two Gaussian Bayesian networks are differential in structure, strength of associations between nodes, or both. Bootstrap procedures are developed to estimate p-values to quantify the statistical significance of the tests. Operating characteristics of these testing procedures are investigated using synthetic data in simulation experiments. Additionally, the proposed methods are applied to flow cytometry data from a designed experiment, and data of bile acids from an observational study in the Alzheimer's Disease Neuroimaging Initiative.
Publication Title
Computational Statistics and Data Analysis
Recommended Citation
Huang, X., & Zhang, H. (2021). Tests for differential Gaussian Bayesian networks based on quadratic inference functions. Computational Statistics and Data Analysis, 159 https://doi.org/10.1016/j.csda.2021.107209
