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CSDA
2010

Bayesian density estimation and model selection using nonparametric hierarchical mixtures

8 years 9 months ago
Bayesian density estimation and model selection using nonparametric hierarchical mixtures
We consider mixtures of parametric densities on the positive reals with a normalized generalized gamma process (Brix, 1999) as mixing measure. This class of mixtures encompasses the Dirichlet process mixture (DPM) model, but it is supposedly more flexible in the detection of clusters in the data. With an almost sure approximation of the posterior distribution of the mixing process we can run a Markov chain Monte Carlo algorithm to estimate linear and nonlinear functionals of the predictive distributions. The best-fitting mixing measure is found by minimizing a Bayes factor for parametric against non-parametric alternatives. We illustrate the method with simulated and hystorical data, finding a tradeoff between the best-fitting model and the correct identification of the number of components in the mixture.
Raffaele Argiento, Alessandra Guglielmi, Antonio P
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2010
Where CSDA
Authors Raffaele Argiento, Alessandra Guglielmi, Antonio Pievatolo
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