Loglinear model selection and human mobility
The Annals of Applied Statistics · 2018 · Volume 12 · Issue 2
Abstract
Methods for selecting loglinear models were among Steve Fienberg’s research interests since the start of his long and fruitful career. After we dwell upon the string of papers focusing on loglinear models that can be partly attributed to Steve’s contributions and influential ideas, we develop a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables. We show how multi-way contingency tables can be used to represent patterns of human mobility. We analyze a dataset of geolocated tweets from South Africa that comprises $46$ million latitude/longitude locations of $476\mbox{,}601$ Twitter users that is summarized as a contingency table with $214$ variables.
Keywords
Bayesian structural learning, Birth–death processes, Contingency tables, graphical models, human mobility, Model selection, pseudo-likelihood
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