Arbeitspapier

Posterior consistency in conditional density estimation by covariate dependent mixtures

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.

Language
Englisch

Bibliographic citation
Series: Reihe Ökonomie / Economics Series ; No. 282

Classification
Wirtschaft
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
Subject
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

Event
Geistige Schöpfung
(who)
Norets, Andriy
Pelenis, Justinas
Event
Veröffentlichung
(who)
Institute for Advanced Studies (IHS)
(where)
Vienna
(when)
2011

Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Norets, Andriy
  • Pelenis, Justinas
  • Institute for Advanced Studies (IHS)

Time of origin

  • 2011

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