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• W2952492922 abstract "Abstract Estimation of finite mixture models when the mixing distribution support is unknown is an important problem. This article gives a new approach based on a marginal likelihood for the unknown support. Motivated by a Bayesian Dirichlet prior model, a computationally efficient stochastic approximation version of the marginal likelihood is proposed and large-sample theory is presented. By restricting the support to a finite grid, a simulated annealing method is employed to maximize the marginal likelihood and estimate the support. Real and simulated data examples show that this novel stochastic approximation and simulated annealing procedure compares favorably with existing methods. Keywords: Dirichlet distributionMixture complexityPredictive recursionSimulated annealingStochastic approximationMathematics Subject Classification: 62G07; 62F15 Acknowledgments The author is grateful to two anonymous reviewers for their thoughtful criticisms that led to an overall improvement of the article. Also special thanks go to Professors J. K. Ghosh, Surya T. Tokdar, and Chuanhai Liu for a number of helpful suggestions. A portion of this work was completed while the author was affiliated with the Department of Mathematical Sciences, Indiana University–Purdue University Indianapolis, Indiana, USA." @default.
• W2952492922 created "2019-06-27" @default.
• W2952492922 creator A5081850677 @default.
• W2952492922 date "2013-08-01" @default.
• W2952492922 modified "2023-10-17" @default.
• W2952492922 title "An Approximate Bayesian Marginal Likelihood Approach for Estimating Finite Mixtures" @default.
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• W2952492922 doi "https://doi.org/10.1080/03610918.2012.667476" @default.
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