Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation

DOI10.1016/j.stamet.2009.09.007zbMath1233.62206OpenAlexW2019536654MaRDI QIDQ537432

Garry Robins, Philippa E. Pattison, Johan H. Koskinen

Publication date: 20 May 2011

Published in: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://www.research.manchester.ac.uk/portal/en/publications/analysing-exponential-random-graph-pstar-models-with-missing-data-using-bayesian-data-augmentation(36299696-ce3a-4b8d-9048-295da632fc49).html

zbMATH Keywords

auxiliary variablessocial network analysis

Mathematics Subject Classification ID

Related Items (10)

A tutorial on methods for the modeling and analysis of social network data ⋮ A semiparametric Bayesian approach to epidemics, with application to the spread of the coronavirus MERS in South Korea in 2015 ⋮ Discussion of “A Tale of Two Datasets: Representativeness and Generalisability of Inference for Samples of Networks” by Pavel N. Krivitsky, Pietro Coletti, and Niel Hens ⋮ A Bayesian approach towards missing covariate data in multilevel latent regression models ⋮ The local structure of globalization ⋮ Model-based clustering of large networks ⋮ Exponential-family models of random graphs: inference in finite, super and infinite population scenarios ⋮ Bayesian model selection for high-dimensional Ising models, with applications to educational data ⋮ Efficient computational strategies for doubly intractable problems with applications to Bayesian social networks ⋮ Outliers and influential observations in exponential random graph models

Uses Software

• rstiefel

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian computation for statistical models with intractable normalizing constants
• Logit models and logistic regressions for social networks. I: An introduction to Markov graphs and \(p^*\)
• Modeling social networks from sampled data
• Conjugate priors for exponential families
• Weak convergence and optimal scaling of random walk Metropolis algorithms
• Markov chains for exploring posterior distributions. (With discussion)
• Logit models and logistic regressions for social networks. III: Valued relations
• Outliers and influential observations in exponential random graph models
• Network models for social influence processes
• On the existence of maximum likelihood estimates in logistic regression models
• Sampling-Based Approaches to Calculating Marginal Densities
• Goodness of Fit of Social Network Models
• Markov Graphs
• The Calculation of Posterior Distributions by Data Augmentation
• A note on A. Albert and J. A. Anderson's conditions for the existence of maximum likelihood estimates in logistic regression models
• An Exponential Family of Probability Distributions for Directed Graphs
• Inference and missing data
• An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

This page was built for publication: Analysing exponential random graph (p-star) models with missing data using Bayesian data augmentation