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 polar sampling, a class of flexibel and robust Monte Carlo integration methods
Abstract: Adaptive Polar Sampling (APS) algorithms are proposed for Bayesian analysis of models with  nonelliptical, possibly, multimodal posterior distributions. A location-scale transformation  and a transformation to polar coordinates are used. After the transformation to polar  coordinates, a Metropolis-Hastings method or, alternatively, an importance sampling  method is applied to sample directions and, conditionally on these, distances are  generated  by inverting the cumulative distribution function. A sequential procedure is  applied to update the initial location and scaling 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 APS algorithms compare favourably with the standard  Metropolis-Hastings and importance samplers in terms of flexibility and robustness. APS is  applied to several econometric and statistical examples. The empirical results for a  regression model with scale contamination, an ARMA-GARCH-Student t model with near  cancellation of roots and heavy tails, a mixture model for economic growth, and a  nonlinear threshold model for industrial production growth confirm the practical  flexibility and robustness of APS.
Creation-Date: 2002-09-17
File-URL: https://repub.eur.nl/pub/555/feweco20020917131720.pdf
File-Format: application/pdf
Series: RePEc:ems:eureir
Number: EI 2002-27
Keywords: Importance sampling, Markov chain Monte Carlo, Polar coordinates
Handle: RePEc:ems:eureir:555