Multilinear Models for Longitudinal Relational Tensor Data

• Activity Number: 289
• Type: Invited
• Date/Time: Tuesday, August 11, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Computing
• Author(s): Peter Hoff*
• Companies: University of Washington
• Keywords: tensor ; array ; network ; Bayes ; covariance ; separable

Abstract

A fundamental aspect of relational data, such as from a social network, is the possibility of dependence among the relations: How A relates to B may possibly depend on how C relates to B or even how C relates to D. We investigate the possibility of estimating such dependencies from weekly international relations data among countries from 2004-2013. Such data can be represented as a multiway array, or tensor. We develop a multilinear regression model for analysis of such tensor-valued data, discuss some of its properties and computational techniques for parameter estimation.