Bayesian model selection: a predictive approach with losses based on distances L1 and L2
A Bayesian model consists of two elements: a sampling model and a prior density. In this paper, we propose a new predictive approach for selecting a Bayesian model through a decision problem. The key idea in the paper is the loss function; we propose to measure the L1 distance (or the squared L2 distance) between the densities we can use for predicting future observations: sampling densities and posterior predictive densities. The method is also applied to the problem of variable selection in a regression model, showing a good behavior.
Volume (Year): 71 (2005)
Issue (Month): 3 (March)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/supportfaq.cws_home/regional|
When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:71:y:2005:i:3:p:257-265. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If references are entirely missing, you can add them using this form.