Statistical Inference in a Directed Network Model With Covariates

原文摘要

Ting Yana http://orcid.org/0000-0001-9830-6646, Binyan Jiangb, Stephen E. Fienbergc* & Chenlei Lengd*a Department of Statistics, Central China Normal University, Wuhan, Chinab Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, Chinac Department of Statistics, Heinz College, Machine Learning Department, Cylab, Carnegie Mellon University, Pittsburgh, PAd Department of Statistics, University of Warwick and Alan Turing Institute, Coventry, UKCONTACT Chenlei Leng C.Leng@warwick.ac.uk Department of Statistics, University of Warwick and Alan Turing Institute, Coventry CV4 7AL, UK.Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/r/JASA.* Shortly after finishing the first draft of this paper, we were saddened to hear Steve Fienberg's death. We dedicate this work to his memory.Supplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.ABSTRACTNetworks 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.

摘要来源：OpenAlex