Spatial autoregression with repeated measurements for social networks. Issue 15 (3rd August 2018)

• Record Type: Journal Article
• Authors: Huang, Danyang; Chang, Xiangyu; Wang, Hansheng
• Is Part Of: Communications in statistics. Volume 47:Issue 15(2018)
• Page Start: 3715
• Page End: 3727
• Publication Date: 2018-08-03
• Subjects: Pseudo-maximum likelihood estimate -- Repeated measurements -- Social autocorrelation -- Social network.; 05C82 -- 94C15; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2017.1361989
• Languages: English
• ISSNs: 0361-0926

Abstract

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.