Non-asymptotic model selection for models of network data with parameter vectors of increasing dimension
From MaRDI portal
Publication:6592791
DOI10.1016/j.jspi.2024.106173MaRDI QIDQ6592791
Sean Eli, Michael Schweinberger
Publication date: 26 August 2024
Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)
Bayesian information criterionrandom graph\(\beta\)-modelextended Bayesian information criterionsparse \(\beta\)-model
Cites Work
- Unnamed Item
- Maximum likelihood estimation in the \(\beta\)-model
- Statistics for high-dimensional data. Methods, theory and applications.
- Random graphs with a given degree sequence
- Estimating the dimension of a model
- Detection thresholds for the \(\beta\)-model on sparse graphs
- Extended BIC for small-n-large-P sparse GLM
- Extended Bayesian information criteria for model selection with large model spaces
- Bayes Factors
- Analysis of Networks via the Sparseβ-model
- A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015
- A central limit theorem in the -model for undirected random graphs with a diverging number of vertices
- Inference using noisy degrees: differentially private \(\beta\)-model and synthetic graphs
This page was built for publication: Non-asymptotic model selection for models of network data with parameter vectors of increasing dimension