publication . Book . 2008

Transdimensional sampling algorithms for Bayesian\ud variable selection in classification problems with\ud many more variables than observations

Lamnisos, Demetris; Griffin, Jim E.; Steel, Mark F. J.;
Open Access English
  • Published: 01 Jan 2008
  • Publisher: University of Warwick. Centre for Research in Statistical Methodology
  • Country: United Kingdom
One flexible technique for model search in probit regression is Markov\ud chain Monte Carlo methodology that simultaneously explores the model and\ud parameter space. The reversible jump sampler is designed to achieve this\ud simultaneous exploration. Standard samplers, such as those based on MC3,\ud often have low model acceptance probabilities when there are many more regressors than observations. Simple changes to the form of the proposal leads\ud to much higher acceptance rates. However, high acceptance rates are often\ud associated with poor mixing of chains. This suggests defining a more general\ud model proposal that allows us to propose models "further" ...
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