Regularization in Regression : Comparing Bayesian and Frequentist Methods in a Poorly Informative Situation
We propose a global noninformative approach for Bayesian variable selection that builds onZellner’s g-priors and is similar to Liang et al. (2008). Our proposal does not require any kindof calibration. In the case of a benchmark, we compare Bayesian and frequentist regularizationapproaches under a low informative constraint when the number of variables is almost equalto the number of observations. The simulated and real dataset experiments we present herehighlight the appeal of Bayesian regularization methods, when compared with alternatives.They dominate frequentist methods in the sense they provide smaller prediction errors whileselecting the most relevant variables in a parsimonious way.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
| Length: | 20 |
| Date of creation: | 2010 |
| Handle: | RePEc:crs:wpaper:2010-43 |
| Contact details of provider: | Postal: 15 Boulevard Gabriel Peri 92245 Malakoff Cedex Phone: 01 41 17 60 81 Web page: http://www.crest.fr More information through EDIRC
|
No references listed on IDEAS
You can help add them by filling out this form.
This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.
When requesting a correction, please mention this item's handle: RePEc:crs:wpaper:2010-43. 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: (Florian Sallaberry)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.