论文标题
具有协变量和越来越多的节点参数的网络模型中的渐近理论
Asymptotic theory in network models with covariates and a growing number of node parameters
论文作者
论文摘要
我们提出了一个通用模型，该模型共同表征了程度异质性，并且在加权，无方向的网络中同质。我们使用节点度和同质统计数据提供了矩估计方法。我们使用新的分析建立了估计量的一致性和渐近正态性。我们将一般框架应用于三个应用程序，包括指数家庭和非指数家庭模型。全面的数值研究和数据示例也证明了我们方法的有用性。
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.
