Estimating the correlation in network disturbance models
Estimating the correlation in network disturbance models
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DOI：
10.1093/comnet/cnab028
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
A. Barbour;G. Reinert
中科院分区：
文献类型：
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作者：
A. Barbour;G. Reinert
P. Doreian（1989）的网络扰动模型通过使用单个相关参数$\rho$对相邻顶点之间的相关性进行建模，来表达在网络顶点处进行的观测之间的依赖性。据观察，估计$\rho$在密集的图形，使用最大似然法，导致结果，可以是有偏见的和非常不稳定的。在这篇文章中，我们将概述为什么会出现这种情况，说明无论网络有多大，可变性都是无法避免的。我们还提出了一个更直观的估计$\rho$，它显示出很小的偏差。对相关的网络效应模型进行了简要的讨论。
The network disturbance model of P. Doreian (1989), expresses the dependency between observations taken at the vertices of a network by modelling the correlation between neighbouring vertices, using a single correlation parameter $\rho$. It has been observed that estimation of $\rho$ in dense graphs, using the method of maximum likelihood, leads to results that can be both biased and very unstable. In this article, we sketch why this is the case, showing that the variability cannot be avoided, no matter how large the network. We also propose a more intuitive estimator of $\rho$, which shows little bias. The related network effects model is briefly discussed.