Borrowing Information in Biometrics Applications via Bayesian Nonparametrics

This dissertation is motivated by three real-world case studies where standard analytical methods proved inadequate due to the complexity and heterogeneity of the data. The limitations of classical approaches led to unreliable inferences, highlighting the need for more flexible methodologies. By employing Bayesian nonparametric models, we address application-specific challenges and develop robust inferential strategies tailored to each context. All proposed methods are supported by simulation studies.