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Activity Number: 535
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #316698
Title: Bayesian Density Regression for Discrete Outcomes
Author(s): Georgios Papageorgiou*
Companies:
Keywords: consistency ; convergence rates ; Dirichlet process mixtures ; joint models ; latent variables
Abstract:

We develop Bayesian models for density regression, with emphasis on discrete and mixed scale outcomes. The approach we describe represents discrete outcomes utilizing continuous latent variables and thresholding these into discrete ones. The directly observed continuous variables, that can be either covariates or response variables, and the latent continuous variables are jointly modeled using Dirichlet process mixtures of multivariate Gaussians. This allows us to establish conditions for posterior consistency and study convergence rates by applying established theoretical results on models for multivariate continuous density estimation. We present a Markov chain Monte Carlo algorithm for posterior sampling and provide illustrations on density and quantile regression and clustering utilizing simulated and real datasets.


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