Event Description
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
For more information, please email nanderson@drexel.edu
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