A Bayesian discovery procedure

Michele Guindani, Peter Müller and Song Zhang

Journal Of The Royal Statistical Society Series B, 2009, vol. 71, issue 5, pages 905-925

Abstract: We discuss a Bayesian discovery procedure for multiple-comparison problems. We show that, under a coherent decision theoretic framework, a loss function combining true positive and false positive counts leads to a decision rule that is based on a threshold of the posterior probability of the alternative. Under a semiparametric model for the data, we show that the Bayes rule can be approximated by the optimal discovery procedure, which was recently introduced by Storey. Improving the approximation leads us to a Bayesian discovery procedure, which exploits the multiple shrinkage in clusters that are implied by the assumed non-parametric model. We compare the Bayesian discovery procedure and the optimal discovery procedure estimates in a simple simulation study and in an assessment of differential gene expression based on microarray data from tumour samples. We extend the setting of the optimal discovery procedure by discussing modifications of the loss function that lead to different single-thresholding statistics. Finally, we provide an application of the previous arguments to dependent (spatial) data. Copyright (c) 2009 Royal Statistical Society.

Date: 2009

Downloads: (external link)
http://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9868.2009.00714.x link to full text (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:bla:jorssb:v:71:y:2009:i:5:p:905-925

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=1369-7412

Access Statistics for this article

Journal Of The Royal Statistical Society Series B is edited by C. Robert and A. T. A. Wood

More articles in Journal Of The Royal Statistical Society Series B from Royal Statistical Society
Series data maintained by Christopher F. Baum ().