Seminar Details

Seminar Details


Sep 20

10:00 am

Modeling Longitudinal Multivariate Data with Mixed Outcomes: Hierarchical Latent Trait and Individual-Level Mixture Models

Jonathan Gruhl

General Exam

University of Washington - Department of Statistics

Advisor: Elena Erosheva

I develop Bayesian hierarchical latent variable models for the study of longitudinal multivariate data. The latent variable models seek to represent multivariate data with a reduced number of dimensions while the hierarchical formulation enables the description of the latent structure evolution over time as well as factors associated with this evolution. Research on cognitive assessments and scientific interest in relating cognitive decline to neuroimaging results and biomarker information motivate these models. To describe cognitive functioning in individuals, I focus on two approaches drawing from unidimensional latent trait models and individual-level mixture models. I extend existing parametric and develop novel semiparametric approaches for these two classes of models to accommodate outcomes of mixed type in cross-sectional and longitudinal settings.