Concentration and consistency results for canonical and curved exponential-family models of random graphs
DOI10.1214/19-AOS1810zbMath1439.05206arXiv1702.01812OpenAlexW3007771191WikidataQ115517744 ScholiaQ115517744MaRDI QIDQ2176626
Michael Schweinberger, Jonathan R. Stewart
Publication date: 5 May 2020
Published in: The Annals of Statistics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1702.01812
exponential familiessocial networkscurved exponential families\(M\)-estimatorsexponential-family random graph modelsmultilevel networks
Social networks; opinion dynamics (91D30) Point estimation (62F10) Random graphs (graph-theoretic aspects) (05C80) Sufficient statistics and fields (62B05)
Related Items (9)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Exponential-family random graph models for valued networks
- On the question of effective sample size in network modeling: an asymptotic inquiry
- Consistency under sampling of exponential random graph models
- Maximum likelihood estimation in the \(\beta\)-model
- Adjusting for network size and composition effects in exponential-family random graph models
- Random graphs with a given degree sequence
- Mixing time of exponential random graphs
- Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
- High-dimensional Ising model selection using \(\ell _{1}\)-regularized logistic regression
- Modeling social networks from sampled data
- Statistical analysis of network data. Methods and models
- The geometry of exponential families
- Defining the curvature of a statistical problem (with applications to second order efficiency)
- Concentration of measure inequalities for Markov chains and \(\Phi\)-mixing processes.
- Likelihood inference in exponential families and directions of recession
- On the geometry of discrete exponential families with application to exponential random graph models
- A statistical framework for modern network science
- Concentration and consistency results for canonical and curved exponential-family models of random graphs
- Inference in Ising models
- Estimating and understanding exponential random graph models
- Estimation in spin glasses: a first step
- Concentration inequalities for dependent random variables via the martingale method
- On Graphical Models via Univariate Exponential Family Distributions
- Instability, Sensitivity, and Degeneracy of Discrete Exponential Families
- A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs
- Goodness of Fit of Social Network Models
- Markov Graphs
- Inference and missing data
- Divide and conquer martingales and the number of triangles in a random graph
- Estimation and Prediction for Stochastic Blockstructures
- Concentration of non‐Lipschitz functions and applications
- The infamous upper tail
- The random triangle model
- Random Networks, Graphical Models and Exchangeability
- Statistical Inference in a Directed Network Model With Covariates
- Local Dependence in Random Graph Models: Characterization, Properties and Statistical Inference
- Asymptotics in directed exponential random graph models with an increasing bi-degree sequence
This page was built for publication: Concentration and consistency results for canonical and curved exponential-family models of random graphs