A1125
Title: A regression framework for studying relationships among attributes under network interference: Statistical theory
Authors: Michael Schweinberger - The Pennsylvania State University (United States) [presenting]
Cornelius Fritz - Trinity College Dublin (Ireland)
Subhankar Bhadra - Pennsylvania State University (United States)
David Hunter - Pennsylvania State University (United States)
Abstract: When network data are collected, the structure of networks is often of secondary interest compared to the question of how networks affect individual or collective outcomes. The well-established class of models known as generalized linear models (GLMs), which includes linear and logistic regression, assumes that the response of a given unit depends on predictors measured on that unit but is unaffected by predictors and responses of other units. A statistical framework that captures complex and realistic dependencies among attributes and connections is introduced while retaining the virtues of GLMs. The framework helps study relationships among attributes under network interference and is applicable to binary, count-valued, and real-valued attributes. Theoretical guarantees are established based on a single observation of dependent attributes and connections. In a companion talk, it is demonstrated that the framework is amenable to scalable statistical computing.
