A NAIVE LEAST SQUARES METHOD FOR SPATIAL AUTOREGRESSION WITH COVARIATES
A NAIVE LEAST SQUARES METHOD FOR SPATIAL AUTOREGRESSION WITH COVARIATES
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协变量空间自回归的朴素最小二乘法
DOI：
10.5705/ss.202017.0135
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发表时间：
2020-04-01
影响因子：
1.4
通讯作者：
Wang, Hansheng
中科院分区：
文献类型：
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作者：
Ma, Yingying;Pan, Rui;Wang, Hansheng
随着社会网络的快速发展，带协变量的空间自回归模型得到了越来越多的应用。然而，当网络规模n很大时，传统的估计方法，如最大似然估计，实际上是不可行的。在这里，我们提出了一种新的估计方法，将计算复杂度从O(n(3))降低到O(N)。该方法是在忽略网络依赖引起的内生性问题的基础上提出的。我们证明了所得到的估计量在一定条件下是一致的和渐近正态的。通过大量的仿真研究来验证它在有限样本下的性能，并分析了一个真实的社会网络数据集作为说明。
The rapid development of social networks has resulted in an increase in the use of the spatial autoregression model with covariates. However, traditional estimation methods, such as the maximum likelihood estimation, are practically infeasible if the network size n is very large. Here, we propose a novel estimation approach, that reduces the computational complexity from O(n(3)) to O(n). This approach is developed by ignoring the endogeneity issue induced by network dependence. We show that the resulting estimator is consistent and asymptotically normal under certain conditions. Extensive simulation studies are presented to demonstrate its finite-sample performance, and a real social network data set is analyzed for illustration purposes.