Asymptotic theory in network models with covariates and a growing number of node parameters
Statistics Theory
2022-07-21 v1 Statistics Theory
Abstract
We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment estimation method using node degrees and homophily statistics. We establish consistency and asymptotic normality of our estimator using novel analysis. We apply our general framework to three applications, including both exponential family and non-exponential family models. Comprehensive numerical studies and a data example also demonstrate the usefulness of our method.
Keywords
Cite
@article{arxiv.2207.09861,
title = {Asymptotic theory in network models with covariates and a growing number of node parameters},
author = {Qiuping Wang and Yuan Zhang and Ting Yan},
journal= {arXiv preprint arXiv:2207.09861},
year = {2022}
}
Comments
48pages, 1 figure. This article supersedes arxiv article 1806.02550 by Ting Yan