SMU SOE Seminar (July 12, 2019): Optimal Auxiliary Priors and Reversible Jump Proposals for a Class of Variable Dimension Models

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ABSTRACT This paper develops a Markov chain Monte Carlo (MCMC) method for a class of models that encompasses finite and countable mixtures of densities and mixtures of experts with a variable number of mixture components. The method is shown to maximize the expected probability of acceptance for cross-dimensional moves and to minimize the asymptotic variance of sample average estimators under certain restrictions. The method can be represented as a retrospective sampling algorithm with an optimal choice of auxiliary priors and as a reversible jump algorithm with optimal proposal distributions. The method is primarily motivated by and applied to a Bayesian nonparametric model for conditional densities based on mixtures of a variable number of experts. Keywords: Bayes, Variable Dimension Model, Reversible Jump, MCMC, Retrospective Sampling, Mixtures, Mixture of Experts, Covariate Dependent Mixture, Kernel Mixture. Click here to view the paper. Click here to view the CV.
		PRESENTER Andriy Norets Brown University
		RESEARCH FIELDS Econometric Theory Bayesian Econometrics Dynamic Discrete Choice Models
		DATE: 12 July 2019 (Friday)
		TIME: 4pm - 5.30pm
		VENUE: Meeting Room 5.1, Level 5 School of Economics Singapore Management University 90 Stamford Road Singapore 178903