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A Bayesian Joint Model Framework for Repeated Matrix-Variate Regression with Measurement Error Correction
DownloadSpring 2021
In this thesis, with the purpose of correcting for potential measurement errors in repeatedly-observed matrix-valued surrogates, and examining the underlying association between latent matrix covariates and a binary response, we propose a Bayesian joint model framework. This joint model method...
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Bayesian hierarchical modeling and its applications to clustering and data privacy preservation
DownloadFall 2024
The evolution of data acquisition technologies and the exponential growth in computing capabilities have inaugurated an epoch wherein researchers are empowered to procure data of unprecedented dimensionality and complexity. Simultaneously, Bayesian hierarchical models distinguish themselves as...