Empirical Likelihood Confidence Intervals for Linear Regression Coefficients
Nonparametric versions of Wilks' theorem are proved for empirical likelihood estimators of slope and mean parameters for a simple linear regression model. They enable us to construct empirical likelihood confidence intervals for these parameters. The coverage errors of these confidence intervals are of order n-1 and can be reduced to order n-2 by Bartlett correction.
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): 49 (1994)
Issue (Month): 1 (April)
|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:jmvana:v:49:y:1994:i:1:p:24-40. 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: (Shamier, Wendy)
If references are entirely missing, you can add them using this form.