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Epidemiology and Biostatistics Dissertation Defense: Guangzi Song

Wednesday, December 14, 2022

11:30 AM-1:30 PM

Guangzi Song, MS, will present "Estimation of the Informativeness of the Conditional Autoregressive Model Framework with Applications"

The use of the conditional autoregressive (CAR) model framework of Besag et al. (1991) is ubiquitous in Bayesian disease mapping. While it is understood that Bayesian inference is based on a combination of the information contained in the data and the information contributed by the model, quantifying the contribution of the model relative to the information in the data is often non-trivial. This dissertation a) provides a measure of the contribution of the BYM framework in the case of binomially distributed count data and a guidance to control the amount of information from the framework of Besag et al., b) reparameterizes the CAR model framework of Besag et al. and proposes a new prior specification for the purpose of controlling the information contributed by the model in a more straightforward way, and c) compares the degree of oversmoothing in multiple models for disease mapping.

Committee Members: Harrison Quick, Loni Tabb, Brisa Sanchez, Usama Bilal, and Leah Schinasi

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Contact Information

Nancy Colon-Anderson

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Nesbitt Hall, Room 751 and via Zoom


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