A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This class, namely mixtures of parametric densities on the positive reals with normalized generalized gamma process as mixing measure, is very flexible in the detection of clusters in the data. With an almost sure approximation of the posterior trajectories the mixing process a Markov chain Monte Carlo algorithm is run to estimate linear and nonlinear functionals of the predictive distributions. The best-fitting mixing measure found by minimizing a Bayes factor for parametric against nonparametric alternatives. Simulated and historical data illustrate the method, finding a trade-off between the best-fitting model and the correct identification of the number of components in the mixture.

(2010). Bayesian density estimation and model selection using nonparametric hierarchical mixtures [journal article - articolo]. In COMPUTATIONAL STATISTICS & DATA ANALYSIS. Retrieved from http://hdl.handle.net/10446/193982

Bayesian density estimation and model selection using nonparametric hierarchical mixtures

Argiento, Raffaele;
2010-01-01

Abstract

A class of nonparametric hierarchical mixtures is considered for Bayesian density estimation. This class, namely mixtures of parametric densities on the positive reals with normalized generalized gamma process as mixing measure, is very flexible in the detection of clusters in the data. With an almost sure approximation of the posterior trajectories the mixing process a Markov chain Monte Carlo algorithm is run to estimate linear and nonlinear functionals of the predictive distributions. The best-fitting mixing measure found by minimizing a Bayes factor for parametric against nonparametric alternatives. Simulated and historical data illustrate the method, finding a trade-off between the best-fitting model and the correct identification of the number of components in the mixture.
articolo
2010
Argiento, Raffaele; Guglielmi, Alessandra; Pievatolo, Antonio
(2010). Bayesian density estimation and model selection using nonparametric hierarchical mixtures [journal article - articolo]. In COMPUTATIONAL STATISTICS & DATA ANALYSIS. Retrieved from http://hdl.handle.net/10446/193982
File allegato/i alla scheda:
File Dimensione del file Formato  
08-12_CSDA_4aperto.pdf

Open Access dal 02/04/2012

Descrizione: link to the formal publication via its DOI: 10.1016/j.csda.2009.11.002
Versione: postprint - versione referata/accettata senza referaggio
Licenza: Creative commons
Dimensione del file 1.11 MB
Formato Adobe PDF
1.11 MB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/193982
Citazioni
  • Scopus 29
  • ???jsp.display-item.citation.isi??? 31
social impact