指数族随机图模型的选择--基于保留预测评价（HOPE）
Selection of Exponential-Family Random Graph Models via Held-Out Predictive Evaluation (HOPE)
摘要：
具有复杂依赖关系的网络的统计模型对模型选择和评估提出了特殊的挑战。特别是，许多成熟的统计工具之间的选择模型假设条件独立的观察和/或传统的渐近性，其理论基础并不总是适用于网络建模环境。虽然基于模拟的模型充分性评估方法现在被广泛使用，但仍然需要以适合于在竞争模型中进行选择的方式量化模型性能的程序。在这里，我们建议解决这个问题，通过开发一个预测评估策略的指数族随机图模型，这是类似于交叉验证。我们的方法建立在Wang等人（2016）引入的保留预测评估（HOPE）计划的基础上，以评估插补性能。我们系统地保留观察到的网络的一部分，以：评估模型能够预测保留的数据的程度;根据哪些数据被保留，识别模型表现不佳的地方，例如指出潜在的弱点;计算可用于模型选择的预测性能的一般摘要。因此，HOPE可以帮助研究人员改进模型，指出模型表现不佳的地方，并定量比较竞争模型的预测性能。将该方法应用于两个著名数据集的模型选择问题，并将结果与通过名义AIC和BIC分数获得的结果进行比较。
英文摘要：
Statistical models for networks with complex dependencies pose particular challenges for model selection and evaluation. In particular, many well-established statistical tools for selecting between models assume conditional independence of observations and/or conventional asymptotics, and their theoretical foundations are not always applicable in a network modeling context. While simulation-based approaches to model adequacy assessment are now widely used, there remains a need for procedures that quantify a model's performance in a manner suitable for selecting among competing models. Here, we propose to address this issue by developing a predictive evaluation strategy for exponential family random graph models that is analogous to cross-validation. Our approach builds on the held-out predictive evaluation (HOPE) scheme introduced by Wang et al. (2016) to assess imputation performance. We systematically hold out parts of the observed network to: evaluate how well the model is able to predict the held-out data; identify where the model performs poorly based on which data are held-out, indicating e.g. potential weaknesses; and calculate general summaries of predictive performance that can be used for model selection. As such, HOPE can assist researchers in improving models by indicating where a model performs poorly, and by quantitatively comparing predictive performance across competing models. The proposed method is applied to model selection problem of two well-known data sets, and the results are compared to those obtained via nominal AIC and BIC scores.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
