Journal Article

Statistical Inference in a Directed Network Model With Covariates

Fienberg, Stephen E., Yan, Ting, Jiang, Binyan, Leng, Chenlei

2019

Overview

Networks are often characterized by node heterogeneity for which nodes exhibit different degrees of interaction and link homophily for which nodes sharing common features tend to associate with each other. In this article, we rigorously study a directed network model that captures the former via node-specific parameterization and the latter by incorporating covariates. In particular, this model quantifies the extent of heterogeneity in terms of outgoingness and incomingness of each node by different parameters, thus allowing the number of heterogeneity parameters to be twice the number of nodes. We study the maximum likelihood estimation of the model and establish the uniform consistency and asymptotic normality of the resulting estimators. Numerical studies demonstrate our theoretical findings and two data analyses confirm the usefulness of our model. Supplementary materials for this article are available online.

• Publisher: Taylor & Francis,Taylor & Francis Group, LLC,Taylor & Francis Ltd
• Subject: Americans / Asymptotic normality / Consistency / Degree heterogeneity / Economic models / exhibitions / Heterogeneity / Homophily / Increasing number of parameters / Inference / Mathematical models / Maximum likelihood estimation / Maximum likelihood estimator / Maximum likelihood method / Networks / Nodes / Normality / Parameterization / Parameters / Regression analysis / Statistical inference / Statistical methods / Statistics / Theory and Methods / Usefulness