On the Theoretical Properties of the Network Jackknife
On the Theoretical Properties of the Network Jackknife
复制标题
论网络折刀的理论性质
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Purnamrita Sarkar
中科院分区：
文献类型：
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作者：
Qiaohui Lin;Robert Lunde;Purnamrita Sarkar
我们研究网络数据的离开节点刀切过程的属性。在稀疏图模型下，我们证明了 Efron-Stein 型不等式，表明网络折刀导致对节点排列不变的任何网络函数的方差（期望中）进行保守估计。对于一般类别的计数泛函，我们还建立了网络折刀的一致性。我们用一系列模拟和真实数据示例补充了我们的理论分析，并表明网络折刀在已知其他重采样方法有效的情况下提供了具有竞争力的性能。事实上，对于一些网络统计数据，我们发现与子采样等相关方法相比，折刀法提供了更准确的推论。
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.