Social Network Distributed Autoregressive Distributed Lag Model

• Activity Number: 215 - Contributed Poster Presentations: Section on Statistical Learning and Data Science
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Christopher Grubb* and Shyam Ranganathan and Srijan Sengupta and Jennifer Van Mullekom
• Companies: Virginia Tech and Virginia Tech and Virginia Tech and Virginia Tech
• Keywords: network; autoregression; distributed lag model; emperical bayes; zero-inflated

Abstract

We leverage a large-scale dynamic social network, in which a continuous response and network state changes are observed on a discrete time scale. We fit an autoregressive distributed lag model in which the response depends on the network structure and its lagged states, lagged responses, and node-specific covariates. Least squares approaches are considered, as well as empirical Bayesian approaches implementing a zero-inflated distribution for the response. The model is developed within the setting of predicting donations to university endowments; however, a number of simulation studies are also conducted. Also, the possibility of extending the model to a binary response is explored.