Eva Cantoni Joanna Mills Flemming Elvezio Ronchetti
Abstract
We adapt Breiman's (1995) nonnegative garrote method to perform variable selection in nonparametric additive models. The technique avoids methods of testing for which no reliable distributional theory is available. In addition it removes the need for a full search of all possible models, something which is computationally intensive, especially when the number of variables is moderate to high. The method has the advantages of being conceptually simple and computationally fast. It provides accurate predictions and is effective at identifying the variables generating the model. For illustration, we consider both a study of Boston housing prices as well as two simulation settings. In all cases our methods perform as well or better than available alternatives like the Component Selection and Smoothing Operator (COSSO).
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. 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: 17 pages Date of creation: Mar 2006 Date of revision: Handle: RePEc:gen:geneem:2006.02
Contact details of provider: Postal: 40 Boulevard du Pont-d'Arve, CH-1211 Geneva 4, Switzerland Phone: +41 22 379-8200 Fax: +41 22 379-8299 Email: Web page: http://www.unige.ch/ses/metri/
For technical questions regarding this item, or to correct its listing, contact: () The email address of this maintainer does not seem to be valid anymore. Please ask to update the entry or send us the correct address..
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.: