Template-Type: ReDIF-Paper 1.0
Author-Name: Bauwens, L.
Author-Name-Last: Bauwens
Author-Name-First: Luc
Author-Person: pba4
Author-Name: Bos, C.S.
Author-Name-Last: Bos
Author-Name-First: Charles
Author-Person: pbo94
Author-Name: van Dijk, H.K.
Author-Name-Last: van Dijk
Author-Name-First: Herman
Author-Person: pva325
Author-Name: van Oest, R.D.
Author-Name-Last: van Oest
Author-Name-First: Rutger
Title: Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods
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.
Creation-Date: 2003-08-06
File-URL: https://repub.eur.nl/pub/1722/feweco20030806161348.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2003-22
Classification-JEL: C11, C15, C63
Keywords: Markov chain Monte Carlo, importance sampling, radial coordinates
Handle: RePEc:ems:eureir:1722