Non-parametric heteroscedastic transformation regression models for skewed data with an application to health care costs
We develop a new non-parametric heteroscedastic transformation regression model for predicting the expected value of the outcome of a patient with given patient's covariates when the distribution of the outcome is highly skewed with a heteroscedastic variance. In our model, we allow both the transformation function and the error distribution function to be unknown. We show that under some regularity conditions the estimators for regression parameters, the expected value of the original outcome and the transformation function converge to their true values at the rate "n"-super- - 1/2. In our simulation studies, we demonstrate that our proposed non-parametric method is robust with little loss of efficiency. Finally, we apply our model to a study on health care costs. Copyright (c) 2008 Royal Statistical Society.
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.
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.
Volume (Year): 70 (2008)
Issue (Month): 5 ()
|Contact details of provider:|| Postal: 12 Errol Street, London EC1Y 8LX, United Kingdom|
Web page: http://wileyonlinelibrary.com/journal/rssb
More information through EDIRC
|Order Information:||Web: http://ordering.onlinelibrary.wiley.com/subs.asp?ref=1467-9868&doi=10.1111/(ISSN)1467-9868|
When requesting a correction, please mention this item's handle: RePEc:bla:jorssb:v:70:y:2008:i:5:p:1029-1047. 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: (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.