含缺失数据的动态网络分析：理论与方法
DYNAMIC NETWORK ANALYSIS WITH MISSING DATA: THEORY AND METHODS.
作者信息
Almquist Zack W, Butts Carter T
机构信息
Department of Sociology and School of Statistics, University of Minnesota, Minnesota 55455, USA.
Departments of Sociology, Statistics, EECS and IMBS, University of California, Irvine, CA 92697, USA.
出版信息
Stat Sin. 2018 Jul;28(3):1245-1264. doi: 10.5705/ss.202016.0108.
Statistical methods for dynamic network analysis have advanced greatly in the past decade. This article extends current estimation methods for dynamic network logistic regression (DNR) models, a subfamily of the Temporal Exponential-family Random Graph Models, to network panel data which contain missing data in the edge and/or vertex sets. We begin by reviewing DNR inference in the complete data case. We then provide a missing data framework for DNR families akin to that of Little and Rubin (2002) or Gile and Handcock (2010a). We discuss several methods for dealing with missing data, including multiple imputation (MI). We consider the computational complexity of the MI methods in the DNR case and propose a scalable, design-based approach that exploits the simplifying assumptions of DNR. We dub this technique the "complete-case" method. Finally, we examine the performance of this method via a simulation study of induced missingness in two classic network data sets.
在过去十年中，动态网络分析的统计方法有了很大进展。本文将当前用于动态网络逻辑回归（DNR）模型（时间指数族随机图模型的一个子族）的估计方法扩展到边集和/或顶点集包含缺失数据的网络面板数据。我们首先回顾完整数据情况下的DNR推断。然后，我们为DNR族提供一个类似于Little和Rubin（2002年）或Gile和Handcock（2010a）的缺失数据框架。我们讨论了几种处理缺失数据的方法，包括多重填补（MI）。我们考虑了DNR情况下MI方法的计算复杂性，并提出了一种可扩展的、基于设计的方法，该方法利用了DNR的简化假设。我们将这种技术称为“完整案例”方法。最后，我们通过对两个经典网络数据集的诱导缺失进行模拟研究，检验了该方法的性能。
Zotero 插件