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A general framework for the parametrisation of hierarchical models
Lancaster University.
Lancaster University.
Örebro University, Department of Business, Economics, Statistics and Informatics.
2007 (English)In: Statistical Science, ISSN 0883-4237, Vol. 22, no 1, 59-73 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we describe centering and noncentering methodology as complementary techniques for use in parametrization of broad classes of hierarchical models, with a view to the construction of effective MCMC algorithms for exploring posterior distributions from these models. We give a clear qualitative understanding as to when centering and noncentering work well, and introduce theory concerning the convergence time complexity of Gibbs samplers using centered and noncentered parametrizations. We give general recipes for the construction of noncentered parametrizations, including an auxiliary variable technique called the state-space expansion technique. We also describe partially noncentered methods, and demonstrate their use in constructing robust Gibbs sampler algorithms whose convergence properties are not overly sensitive to the data.

Place, publisher, year, edition, pages
2007. Vol. 22, no 1, 59-73 p.
Keyword [en]
Parametrization, hierarchical models, latent stochastic processes, MCMC
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:oru:diva-5710DOI: 10.1214/088342307000000014OAI: oai:DiVA.org:oru-5710DiVA: diva2:173936
Available from: 2009-02-18 Created: 2009-02-18 Last updated: 2010-11-01Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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