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An approximate Bayesian marginal likelihood approach for estimating nite mixtures

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posted on 07.08.2014 by Ryan Martin
Estimation of nite mixture models when the mixing distribution support is unknown is an important problem. This paper gives a new approach based on a marginal likelihood for the unknown support. Motivated by a Bayesian Dirich- let prior model, a computationally e cient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented. By restricting the support to a nite grid, a simulated annealing method is employed to maximize the marginal likelihood and estimate the support. Real and simulated data exam- ples show that this novel stochastic approximation{simulated annealing procedure compares favorably to existing methods.

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Publisher Statement

Post print version of article may differ from published version. This is an electronic version of an article published in Communications in Statistics - Simulation and Computation. Martin R. An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures. Communications in Statistics-Simulation and Computation. Aug 2013;42(7):1533-1548. Communications in Statistics - Simulation and Computation is available online at: http://www.informaworld.com/smpp/ DOI:10.1080/03610918.2012.667476

Publisher

Taylor & Francis

Language

en_US

issn

0361-0918

Issue date

01/08/2013

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