Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods

Luc Bauwens, Charles Bos, Herman van Dijk and Rutger van Oest

No EI 2003-22, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with nonelliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution by means of the numerical inverse transformation method. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the ARDS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. The empirical examples include a regression model with scale contamination and a mixture model for economic growth of the USA.

Keywords: Markov chain Monte Carlo; importance sampling; radial coordinates (search for similar items in EconPapers)
JEL-codes: C11 C15 C63 (search for similar items in EconPapers)
Date: 2003-08-06
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Citations: View citations in EconPapers (1)

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Journal Article: Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods (2004)
Working Paper: Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods (2004)
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