For a better experience, click the Compatibility Mode icon above to turn off Compatibility Mode, which is only for viewing older websites.

Department of Epidemiology and Biostatistics Dissertation Defense

Monday, May 20, 2024

1:00 PM-3:00 PM

"Bayesian Spatial Statistical Methods for Operationalizing and Estimating the Impact of Racialized Economic Segregation in the US" by Yang Xu, MS, Department of Epidemiology and Biostatistics.

Dissertation committee: Loni Philip Tabb, Leslie McClure, Issa Zakeri, Harrison Quick, Jaquelyn (Jackie) Jahn, and Irene Headen

This dissertation consists of three studies that focus on the features and statistical assumptions of the Index of Concentration at the Extremes (ICE)—an operationalization of racialized economic segregation. Bayesian statistical frameworks, including parametric and semi-parametric methods, are developed to examine the relationship between the Index of Concentration at the Extremes and various health outcomes. We start by developing a two–stage Bayesian statistical framework that provides a broad, flexible approach to studying the spatially varying association between premature mortality and ICE while accounting for neighborhood–level latent health factors across US counties. Then, we propose a Bayesian semi–parametric model for estimating the varying relationship between ICE and COVID–19 death, which allows the effect of racialized economic segregation to vary based on effect modifiers. Lastly, we propose reformulating the ICE metric using a Bayesian methodological framework, which will enable us to quantify the uncertainty and spatial correlation in the data without losing the original interpretation of the ICE metric.

Yang is a PhD candidate in Biostatistics working under the supervision of Dr. Loni Philip Tabb. During her doctoral program, Yang’s research focused on using Bayesian spatial model for analyzing segregation data.

Zoom link

Contact Information

Nancy Colon-Anderson

Remind me about this event. Notify me if this event changes. Add this event to my personal calendar.


Hagerty Library, L33 Learning Lab and via Zoom


  • Everyone