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.
- Sprache
-
Englisch
- Erschienen in
-
Series: Reihe Ökonomie / Economics Series ; No. 282
- Klassifikation
-
Wirtschaft
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
- Thema
-
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
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Norets, Andriy
Pelenis, Justinas
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute for Advanced Studies (IHS)
- (wo)
-
Vienna
- (wann)
-
2011
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:41 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Norets, Andriy
- Pelenis, Justinas
- Institute for Advanced Studies (IHS)
Entstanden
- 2011
Ähnliche Objekte (12)
On Conditional Density Estimation
A Consistency Theorem for Regular Conditional Distributions
Modeling conditional densities using finite smooth mixtures
Superfluid density in metastable 3He-4He mixtures
Explicit results on conditional distributions of generalized exponential mixtures
Consistency of the kernel density estimator - a survey
Consistency of the kernel density estimator - a survey
Density and conditional distribution based specification analysis
Density-conditional forecasts in dynamic multivariate models
Regression density estimation using smooth adaptive Gaussian mixtures
Regression density estimation using smooth adaptive Gaussian mixtures
Asymptotic normality and consistency of orthogonal polynomial density estimators
On Conditional Density Estimation
A Consistency Theorem for Regular Conditional Distributions
Modeling conditional densities using finite smooth mixtures
Superfluid density in metastable 3He-4He mixtures
Explicit results on conditional distributions of generalized exponential mixtures
Consistency of the kernel density estimator - a survey
Consistency of the kernel density estimator - a survey
Density and conditional distribution based specification analysis
Density-conditional forecasts in dynamic multivariate models
Regression density estimation using smooth adaptive Gaussian mixtures
Regression density estimation using smooth adaptive Gaussian mixtures
Asymptotic normality and consistency of orthogonal polynomial density estimators
On Conditional Density Estimation
A Consistency Theorem for Regular Conditional Distributions
Modeling conditional densities using finite smooth mixtures
Superfluid density in metastable 3He-4He mixtures
Explicit results on conditional distributions of generalized exponential mixtures
Consistency of the kernel density estimator - a survey
Consistency of the kernel density estimator - a survey
Density and conditional distribution based specification analysis
Density-conditional forecasts in dynamic multivariate models
Regression density estimation using smooth adaptive Gaussian mixtures
Regression density estimation using smooth adaptive Gaussian mixtures