A0878
Title: Variable selection in mixture models via stochastic partitioning
Authors: Mahlet Tadesse - Georgetown University (United States) [presenting]
Abstract: Identifying latent classes and component-specific relevant predictors can shed important insights when analyzing high-dimensional data. We will present methods to address this problem in a unified manner by combining ideas of mixture models and variable selection and fitting the models via stochastic partitioning. We will illustrate the performance of the methods in different application areas.
