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An approximate Bayesian marginal likelihood approach for estimating nite mixtures

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journal contribution
posted on 07.08.2014, 00:00 authored 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.


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: DOI:10.1080/03610918.2012.667476


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