Testing and Estimation of Social Network Dependence With Time to Event Data

• 成果类型: Article
• 署名作者: Su, Lin; Lu, Wenbin; Song, Rui; Huang, Danyang
• 署名单位: North Carolina State University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2019.1617153
• 发表日期: 2020
• 页码: 570-582
• 关键词: maximum-likelihood-estimation; stochastic blockmodels; cox regression; models; identification

摘要

Nowadays, events are spread rapidly along social networks. We are interested in whether people's responses to an event are affected by their friends' characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent indicator to characterize whether a person's survival time might be affected by his or her friends' features. We first propose a score-type test for detecting the existence of social network dependence. If it exists, we further develop an EM-type algorithm to estimate the model parameters. The performance of the proposed test and estimators are illustrated by simulation studies and an application to a time-to-event dataset about playing a popular mobile game from one of the largest online social network platforms. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.