Title(题名):  Estimating Spatial Autocorrelation With Sampled Network Data
Authors(作者):  Jing Zhou, Yundong Tu, Yuxin Chen, and Hansheng Wang
Source title(刊名):  Journal of Business &Economic Statistics:A Publication of the American Statistical Association
Volume, Issue, Issue date
Pages(页码):  p130-138
ISSN:  0735-0015
Abstract(摘要):  Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc.). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so, network relationships (i.e., edges) involving unsampled nodes are overlooked. This leads to distorted network structure and underestimated spatial autocorrelation. To solve the problem, we propose here a novel solution. By temporarily assuming that the spatial autocorrelation is small, we are able to approximate the likelihood function by its first-order Taylor’s expansion. This leads to the method of approximate maximum likelihood estimator (AMLE). Which further inspires the development of paired maximum likelihood estimator (PMLE). Compared with AMLE, PMLE is computationally superior and thus is particularly useful for large-scale network data analysis. Under appropriate regularity conditions (without assuming a small spatial autocorrelation), we show theoretically that PMLE is consistent and asymptotically normal. Numerical studies based on both simulated and real datasets are presented for illustration purpose.
Key words(关键词):  Approximate maximum likelihood estimator; Network data analysis; Paired maximum likelihood estimator; Spatial autocorrelation.
Where(馆藏地):  304外文报刊阅览室
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