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Norets, Andriy
Pelenis, Justinas
Year of Publication: 
Series/Report no.: 
Economics Series, Institute for Advanced Studies 282
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.
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
Document Type: 
Working Paper

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