Markov chain Monte Carlo

Generalised Linear Models Incorporating Population Level Information: An Empirical Likelihood Based Approach

In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent variable and explanatory variables. Inclusion of the population level information can reduce bias …

Goodness of Fit of Social Network Models

We present a systematic examination of a real network data set using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed networks. These procedures compare …

Degree distributions in sexual networks: A framework for evaluating evidence

Objective: We present a likelihood based statistical framework to test the fit of power-law and alternative social process models for the degree distribution, and derive the sexually transmitted infection epidemic predictions from each model. Study …

Inference in curved exponential family models for networks

Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the science of fitting statistical models to complex network data is still in its infancy. The models considered in this …

Positional Estimation Within a Latent Space Model for Networks

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently …

Inference in curved exponential family models for networks

Network data arise in a wide variety of applications. Although descriptive statistics for networks abound in the literature, the science of fitting statistical models to complex network data is still in its infancy. The models considered in this …

Goodness of Fit of Social Network Models

We present a systematic examination of real network datasets using maximum likelihood estimation for exponential random graph models as well as new procedures to evaluate how well the models fit the observed graphs. These procedures compare …

Positional Estimation Within a Latent Space Model for Networks

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently …

Assessing Degeneracy in Statistical Models of Social Networks

This paper presents recent advances in the statistical modeling of random graphs that have an impact on the empirical study of social networks. Statistical exponential family models (Wasserman and Pattison 1996) are a generalization of the Markov …

Statistical Models for Social Networks: Inference and Degeneracy

This is a paper in the 'Dynamic Social Network Modeling and Analysis', edited by R. Breiger, K. Carley, and P. Pattison. It is the result of the Committee on Human Factors, Board on Behavioral, Cognitive, and Sensory Sciences. National Academy Press: …