Consistently Estimating Graph Statistics Using Aggregated Relational Data

• Activity Number: 1 - Invited E-Poster Session
• Type: Invited
• Date/Time: Sunday, August 2, 2020 : 12:30 PM to 3:30 PM
• Sponsor: Section on Statistical Learning and Data Science
• Author(s): Tyler McCormick* and Arun Chandrasekhar and Emily Breza and Mengji Pan
• Companies: University of Washington and Stanford University and Harvard University and Mengjie Pan
• Keywords: aggregated relational data; social network

Abstract

Aggregated Relational Data, known as ARD, capture information about a social network by asking about the number of connections between a person and a group with a particular characteristic, rather than asking about connections between each pair of individuals directly. Breza et al. (Forthcoming) and McCormick and Zheng (2015) relate ARD questions, consisting of survey items of the form "How many people with characteristic X do you know?" to parametric statistical models for complete graphs. In this paper, we propose criteria for consistent estimation of individual and graph level statistics from ARD data.