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

Model search in probit regression is often conducted by simultaneously exploring the model and parameter space, using a reversible jump MCMC sampler. Standard samplers often have low model acceptance probabilities when there are many more regressors than observations. Implementing recent suggestions in the literature leads to much higher acceptance rates. However, high acceptance rates are often associated with poor mixing of chains. Thus, we design a more general model proposal that allows us to propose models "further" front our current model. This proposal can be tuned to achieve a suitable acceptance rate for good mixing. The effectiveness of this proposal is linked to the form of the marginalization scheme when updating the model and we propose a new efficient implementation of the automatic generic transdimensional algorithm of Green (2003). We also implement other previously proposed samplers and compare the efficiency of all methods on some gene expression datasets. Finally, the results of these applications lead us to propose guidelines for choosing between samplers. Relevant code and datasets are posted as an online supplement