Regression density estimation using smooth adaptive Gaussian mixtures
Abstract: We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heteroscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulat
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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Postprint
begutachtet (peer reviewed)
In: Journal of Econometrics ; 153 (2009) 2 ; 155-173
- Ereignis
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Veröffentlichung
- (wo)
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Mannheim
- (wann)
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2009
- Urheber
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Villani, Mattias
Kohn, Robert
Giordani, Paolo
- DOI
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10.1016/j.jeconom.2009.05.004
- URN
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urn:nbn:de:0168-ssoar-254022
- Rechteinformation
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Open Access unbekannt; Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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25.03.2025, 13:45 MEZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Villani, Mattias
- Kohn, Robert
- Giordani, Paolo
Entstanden
- 2009
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