Spatial autoregression with repeated measurements for social networks

Danyang Huang, Xiangyu Chang and Hansheng Wang

Communications in Statistics - Theory and Methods, 2018, vol. 47, issue 15, 3715-3727

Abstract: Spatial autoregressive model (SAR) is found useful to estimate the social autocorrelation in social networks recently. However, the rapid development of information technology enables researchers to collect repeated measurements for a given social network. The SAR model for social networks is designed for cross-sectional data and is thus not feasible. In this article, we propose a new model which is referred to as SAR with random effects (SARRE) for social networks. It could be considered as a natural combination of two types of models, the SAR model for social networks and a particular type of mixed model. To solve the problem of high computational complexity in large social networks, a pseudo-maximum likelihood estimate (PMLE) is proposed. The asymptotic properties of the estimate are established. We demonstrate the performance of the proposed method by extensive numerical studies and a real data example.

Date: 2018
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DOI: 10.1080/03610926.2017.1361989

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