We begin by defining and providing examples of network-indexed data. We then turn to two applications: modeling occurrences of residential burglary in Boston and identifying network predictors of injection drug use cessation among a group of drug users in rural Kentucky. We discuss the graph Laplacian, a hierarchical regression model and generalized estimating equations along the way.
Bio: Elizabeth Upton completed her Ph.D. in statistics at Boston University. Her research focuses on network science, particularly adapting regression methodologies to network-indexed data. Before attending BU, Elizabeth taught high school math for three years and worked in finance as a quantitative analyst. She currently is an assistant professor at Williams College. Outside of her research and work interests, Elizabeth enjoys spending time with her family, skiing and cheering on the New England Patriots.
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