A General Framework for the Parametrization of Hierarchical Models

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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.
Published at http://dx.doi.org/10.1214/088342307000000014 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Methodology

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http://salesiana.dossiersoluciones.com/handle/123456789/136894

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0708.3797
 http://arxiv.org/abs/0708.3797
 Statistical Science 2007, Vol. 22, No. 1, 59-73
 doi:10.1214/088342307000000014

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arXiv