论文标题
关于网络折刀的理论特性
On the Theoretical Properties of the Network Jackknife
论文作者
论文摘要
我们研究了网络数据的放置折刀程序的属性。在稀疏的Graphon模型下，我们证明了EFRON-Stein型不平等，表明网络折刀会导致对任何不变的网络函数对节点置换的差异的保守估计（预期）。对于一般的计数功能，我们还建立了网络折刀的一致性。我们通过一系列模拟和真实数据示例对理论分析进行补充，并表明，在已知其他重新采样方法有效的情况下，网络折刀会提供竞争性能。实际上，对于几个网络统计数据，我们看到折刀提供了与相关方法（例如子采样）相比提供的更准确的推论。
We study the properties of a leave-node-out jackknife procedure for network data. Under the sparse graphon model, we prove an Efron-Stein-type inequality, showing that the network jackknife leads to conservative estimates of the variance (in expectation) for any network functional that is invariant to node permutation. For a general class of count functionals, we also establish consistency of the network jackknife. We complement our theoretical analysis with a range of simulated and real-data examples and show that the network jackknife offers competitive performance in cases where other resampling methods are known to be valid. In fact, for several network statistics, we see that the jackknife provides more accurate inferences compared to related methods such as subsampling.
