Penalized Regression with Ordinal Predictors
AbstractOrdered categorial predictors are a common case in regression modelling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this paper, existing methods are reviewed and the use of penalized regression techniques is proposed. Based on dummy coding two types of penalization are explicitly developed; the first imposes a difference penalty, the second is a ridge type refitting procedure. Also a Bayesian motivation is provided. The concept is generalized to the case of non-normal outcomes within the framework of generalized linear models by applying penalized likelihood estimation. Simulation studies and real world data serve for illustration and to compare the approaches to methods often seen in practice, namely simple linear regression on the group labels and pure dummy coding. Especially the proposed difference penalty turns out to be highly competitive. Copyright (c) 2009 The Authors. Journal compilation (c) 2009 International Statistical Institute.
Download InfoIf 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.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by International Statistical Institute in its journal International Statistical Review.
Volume (Year): 77 (2009)
Issue (Month): 3 (December)
Contact details of provider:
Postal: P.O. Box 950, 2270 AZ Voorburg
Web page: http://www.blackwellpublishing.com/journal.asp?ref=0306-7734
More information through EDIRC
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Hess, Wolfgang & Persson, Maria & Rubenbauer, Stephanie & Gertheiss, Jan, 2013.
"Using Lasso-Type Penalties to Model Time-Varying Covariate Effects in Panel Data Regressions - A Novel Approach Illustrated by the 'Death of Distance' in International Trade,"
2013:5, Lund University, Department of Economics.
- Hess, Wolfgang & Persson, Maria & Rubenbauer, Stephanie & Gertheiss, Jan, 2013. "Using Lasso-Type Penalties to Model Time-Varying Covariate Effects in Panel Data Regressions – A Novel Approach Illustrated by the ‘Death of Distance’ in International Trade," Working Paper Series 961, Research Institute of Industrial Economics.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).
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.