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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
Abstract #314049
Title: Social Network Distributed Autoregressive Distributed Lag Model
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

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.

Authors who are presenting talks have a * after their name.

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