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
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Englisch
- Bibliographic citation
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Series: Reihe Ökonomie / Economics Series ; No. 282
- Classification
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Wirtschaft
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
- Subject
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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
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Geistige Schöpfung
- (who)
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Norets, Andriy
Pelenis, Justinas
- Event
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Veröffentlichung
- (who)
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Institute for Advanced Studies (IHS)
- (where)
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Vienna
- (when)
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2011
- Handle
- Last update
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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