Adaptive radial-based direction sampling : a class of flexible and robust Monte Carlo integration methods
(2004) Journal of Econometrics — Vol. 123, n° 2, p. 201-225 (2004)
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- Authors
Bauwens, Luc 
- Abstract
- Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with non-elliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformation 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. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. The ARDS algorithms are illustrated on a regression model with scale contamination and a mixture model for economic growth of the USA.
- Affiliations
Free University Amsterdam Department of Econometrics & O.R. Erasmus University Rotterdam Econometric and Tinbergen Institutes Erasmus University Rotterdam Econometric and Tinbergen Institutes
Citations
Bauwens, L., Bos, C., van Dijk, H., & van Oest, R. (2004). Adaptive radial-based direction sampling : a class of flexible and robust Monte Carlo integration methods. Journal of Econometrics, 123(2), 201-225. https://doi.org/10.1016/j.jeconom.2003.12.002 (Original work published 2004)