Most inference using social network models assumes that the presence or absence of all relations is known. This is rarely the case. Most social network analysis ignores the problem of missing data by including only actors with complete observations. …
In this paper, we illustrate that combining ecological data with subsample data in situations in which a linear model is appropriate provides three main benefits. First, by including the individual level subsample data, the biases associated with …
In many situations information from a sample of individuals can be supplemented by population level information on the relationship between a dependent and explanatory variables. Inclusion of the population level information can reduce bias and …
Network models are widely used to represent relations among interacting units or actors. Network data often exhibit transitivity, meaning that two actors that have ties to a third actor are more likely to be tied than actors that do not, homophily by …
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 …
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 …
The most promising class of statistical models for expressing structural properties of social networks is the class of Exponential Random Graph Models (ERGMs), also known as $p^*$ models. The strong point of these models is that they can represent …
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 …
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 …
Regression coefficients specify the partial effect of a regressor on the dependent variable. Sometimes the bivariate, or limited multivariate relationship of that regressor variable with the dependent variable is known from population-level data. We …