Direct estimation of differential networks
From MaRDI portal
Publication:2874942
DOI10.1093/biomet/asu009zbMath1452.62865OpenAlexW2030391580WikidataQ35647249 ScholiaQ35647249MaRDI QIDQ2874942
No author found.
Publication date: 13 August 2014
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc4443936
Estimation in multivariate analysis (62H12) Asymptotic properties of nonparametric inference (62G20) Applications of statistics to biology and medical sciences; meta analysis (62P10) Mathematical geography and demography (91D20)
Related Items (19)
Covariate-adjusted inference for differential analysis of high-dimensional networks ⋮ A convex optimization approach to high-dimensional sparse quadratic discriminant analysis ⋮ Tests for differential Gaussian Bayesian networks based on quadratic inference functions ⋮ Gaussian Bayesian network comparisons with graph ordering unknown ⋮ Covariance matrix testing in high dimension using random projections ⋮ Differential network inference via the fused D-trace loss with cross variables ⋮ A fast iterative algorithm for high-dimensional differential network ⋮ Brain connectivity alteration detection via matrix‐variate differential network model ⋮ Transfer Learning in Large-Scale Gaussian Graphical Models with False Discovery Rate Control ⋮ Comparing dependent undirected Gaussian networks ⋮ Compressed spectral screening for large-scale differential correlation analysis with application in selecting glioblastoma gene modules ⋮ Searching for a source of difference in graphical models ⋮ Data science, big data and statistics ⋮ Unnamed Item ⋮ Unnamed Item ⋮ Post-processing posteriors over precision matrices to produce sparse graph estimates ⋮ Efficient computation for differential network analysis with applications to quadratic discriminant analysis ⋮ Network differential connectivity analysis ⋮ Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach
This page was built for publication: Direct estimation of differential networks
