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Computational efficiency in Bayesian model and variable selection

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dc.contributor Karlsson, Sune is
dc.contributor.author Eklund, Jana is
dc.date.accessioned 2013-12-20T13:25:34Z
dc.date.available 2013-12-20T13:25:34Z
dc.date.issued 2007-04
dc.identifier.issn 1028-9445
dc.identifier.uri http://hdl.handle.net/10802/4767
dc.description Útdráttur á ensku is
dc.description Myndefni: línurit, töflur is
dc.description.abstract This paper is concerned with the eficient implementation of Bayesian model averaging (BMA) and Bayesian variable selection, when the number of candidate variables and models is large, and estimation of posterior model probabilities must be based on a subset of the models. Eficient implementation is concerned with two issues: the eficiency of the MCMC algorithm itself and eficient computation of the quantities needed to obtain a draw from the MCMC algorithm. For the first aspect, it is desirable that the chain moves well and quickly through the model space and takes draws from regions with high probabilities. In this context there is a natural trade-off between local moves, which make use of the current parameter values to propose plausible values for model parameters, and more global transitions, which potentially allow exploration of the distribution of interest in fewer steps, but where each step is more computationally intensive. We assess the convergence properties of simple samplers based on local moves and some recently proposed algorithms intended to improve on the basic samplers. For the second aspect, eficient computation within the sampler, we focus on the important case of linear models where the computations essentially reduce to least squares calculations. When the chain makes local moves, adding or dropping a variable, substantial gains in eficiency can be made by updating the previous least squares solution. en
dc.format.extent 44 s. is
dc.language.iso en
dc.publisher Central Bank of Iceland, Economics Department is
dc.relation.ispartofseries Central Bank of Iceland., Working papers ; 35
dc.relation.uri http://www.sedlabanki.is/lisalib/getfile.aspx?itemid=5156
dc.subject Gagnavinnsla is
dc.subject Tölfræði is
dc.title Computational efficiency in Bayesian model and variable selection en
dc.type Skýrsla is
dc.identifier.gegnir 991004720949706886


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