论文标题
估计网络干扰模型中的相关性
Estimating the correlation in network disturbance models
论文作者
论文摘要
Doreian（1989）的网络干扰模型通过使用单个相关参数$ρ$对相邻顶点之间的相关性进行建模，从而表达了在网络顶点进行的观测值之间的依赖关系。已经观察到，使用最大似然的方法对$ρ$ $ρ$的估计会导致既有偏见又非常不稳定的结果。在本文中，我们素描为什么是这种情况，表明无论网络多大，都无法避免变异性。我们还提出了一个更直观的估计器的$ρ$，这几乎没有偏见。简要讨论了相关的网络效应模型。
The Network Disturbance Model of 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 $ρ$. It has been observed that estimation of $ρ$ in dense graphs, using the method of Maximum Likelihood, leads to results that can be both biased and very unstable. In this paper, 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 $ρ$, which shows little bias. The related Network Effects Model is briefly discussed.
