Posterior Consistency in Conditional Density Estimation by Covariate Dependent Mixtures

Norets, Andriy and Pelenis, Justinas (December 2011) Posterior Consistency in Conditional Density Estimation by Covariate Dependent Mixtures. IHS Economics Series 282

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Abstract or Table of Contents

Abstract: This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strongly consistent for a large class of data generating processes.;

Item Type: IHS Series
Keywords: 'Bayesian nonparametrics' 'Posterior consistency' 'Conditional density estimation' 'Mixtures of normal distributions' 'Location-scale mixtures' 'Smoothly mixing regressions' 'Mixtures of experts' 'Dependent Dirichlet process' 'Kernel stick-breaking process'
Classification Codes (e.g. JEL): C11, C14
Status: Published
Date Deposited: 26 Sep 2014 10:39
Last Modified: 22 Jul 2017 00:08
URI: http://irihs.ihs.ac.at/id/eprint/2108

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