Online Program Home
  My Program

Abstract Details

Activity Number: 120 - SPEED: Variable Selection and Networks
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Learning and Data Science
Abstract #322522
Title: Dynamic Latent Factor Modeling of UN Voting Networks
Author(s): Bomin Kim* and Xiaoyue Niu and David Hunter and Xun Cao
Companies: Pennsylvania State University and Penn State University and The Pennsylvania State University and The Pennsylvania State University
Keywords: latent factor model ; social network analysis ; dynamic network model
Abstract:

The General Assembly of the United Nations serves as the key component of policymaking of the United Nations. 193 members vote on various of events each year. Nations' foreign policy preferences and positions can be revealed from those voting behaviors. In this paper, we analyze the UN voting data (Voeten 2013) using a dynamic latent factor network model. We introduce an extension of the multiplicative latent factor model (Hoff 2009), by incorporating the time-dependent covariance structure to the prior specifications of the parameters. By controlling for geographic distances and bilateral trade between countries, the model estimated latent positions and movement of positions reveal interesting and meaningful foreign policy positions and alliance of various countries.


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

Back to the full JSM 2017 program

 
 
Copyright © American Statistical Association