Arbeitspapier
Adaptive Bayesian estimation of mixed discrete-continuous distributions under smoothness and sparsity
We consider nonparametric estimation of a mixed discrete-continuous distribution under anisotropic smoothness conditions and possibly increasing number of support points for the discrete part of the distribution. For these settings, we derive lower bounds on the estimation rates in the total variation distance. Next, we consider a nonparametric mixture of normals model that uses continuous latent variables for the discrete part of the observations. We show that the posterior in this model contracts at rates that are equal to the derived lower bounds up to a log factor. Thus, Bayesian mixture of normals models can be used for optimal adaptive estimation of mixed discretecontinuous distributions.
- Language
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Englisch
- Bibliographic citation
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Series: IHS Economics Series ; No. 342
- Classification
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Wirtschaft
Bayesian Analysis: General
Semiparametric and Nonparametric Methods: General
- Subject
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Bayesian nonparametrics
adaptive rates
minimax rates
posterior contraction
discretecontinuous distribution
mixed scale
mixtures of normal distributions
latent variables
- 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)
-
2018
- Handle
- Last update
-
10.03.2025, 11:43 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Norets, Andriy
- Pelenis, Justinas
- Institute for Advanced Studies (IHS)
Time of origin
- 2018
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