Adaptive Radial-based Direction Sampling: Some Flexible and Robust Monte Carlo Integration Methods

C.S. Bos, L.J. Bauwens, H.K. van Dijk, R.D. van Oest

    Research output: Contribution to JournalArticleAcademic

    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. © 2003 Elsevier B.V. All rights reserved.
    Original languageEnglish
    Pages (from-to)201-225
    Number of pages25
    JournalJournal of Econometrics
    Volume123
    Issue number2
    DOIs
    Publication statusPublished - 2004

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