用简单诊断评估聚合关系数据模型
Evaluating Aggregated Relational Data Models with Simple Diagnostics
AI总结：
本文提出了一种用于评估聚合关系数据模型的诊断框架，通过点估计和优化方法快速评估模型拟合情况，帮助研究人员选择合适的模型。
摘要：
聚合关系数据（ARD）包含关于个体社交网络的总结信息，被广泛用于估计社交网络特征和目标人群规模。尽管存在多种ARD估计器，但从业者目前缺乏评估所选模型是否充分拟合数据的指导。我们介绍了一种用于ARD模型的诊断框架，提供了一种系统、可重复的过程来评估协变量结构、分布假设和相关性。这些诊断基于点估计，使用最大似然或最大后验优化，允许快速评估而无需重复贝叶斯模型拟合。通过模拟研究和应用于大型ARD数据集，我们证明了所提出的流程能够识别常见的模型不拟合源，并帮助研究人员选择一个能够充分解释数据的合适模型。
英文摘要：
Aggregated Relational Data (ARD) contain summary information about individual social networks and are widely used to estimate social network characteristics and the size of populations of interest. Although a variety of ARD estimators exist, practitioners currently lack guidance on how to evaluate whether a selected model adequately fits the data. We introduce a diagnostic framework for ARD models that provides a systematic, reproducible process for assessing covariate structure, distributional assumptions, and correlation. The diagnostics are based on point estimates, using either maximum likelihood or maximum a posteriori optimization, which allows quick evaluation without requiring repeated Bayesian model fitting. Through simulation studies and applications to large ARD datasets, we show that the proposed workflow identifies common sources of model misfit and helps researchers select an appropriate model that adequately explains the data.
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