Statistics Colloquium: David Dunson
11:30 am–12:30 pm Jones 303
David Dunson, Statistical Science and Department of Mathematics, Duke University
Title: Deep latent class regression
Abstract: High-dimensional categorical data arise in diverse scientific domains and are often accompanied by covariates. Latent class regression models are routinely used in such settings, reducing dimensionality by assuming conditional independence of the categorical variables given a single latent class that depends on covariates through a logistic regression model. However, such methods become unreliable as the dimensionality increases. To address this, we propose a flexible family of deep latent class models. Our model satisfies key theoretical properties, including identifiability and posterior consistency, and we establish a Bayes oracle clustering property that ensures robustness against the curse of dimensionality. We develop efficient posterior computation methods, validate them through simulation studies, and apply our model to joint species distribution modeling in ecology. The theory and methods can be easily extended beyond categorical observed data.
Joint work with Yuren Zhou & Yuqi Gu
Student Seminars: Rohan Hore
1:00–3:00 pm Searle 236
Monday, April 7, 2025, at 1:00 PM, in Searle 236, 5735 S. Ellis Avenue
PhD Dissertation Defense Presentation
Rohan Hore, Department of Statistics, The University of Chicago
“TBA”
