A refinement to approximate conditional inference
AbstractThis manuscript considers inference on a single parameter in a multivariate canonical exponential family, where the effect of nuisance parameters on the p-value is mitigated by conditioning on the event that the sufficient statistics associated with the nuisance parameters lie in a neighborhood about the observed value. This manuscript has three aims. First, we provide a method for approximating p-values using approximate conditioning that is more accurate than that presented by Pierce and Peters (Biometrika 86(1999) 265-277), at the price of greater computational difficulty. Second, we examine the sensitivity of approximate conditioning methods to the values of the nuisance parameters. Third, we describe a method for presenting a valid approximate-conditioning observed significance level accounting for this dependence on the nuisance parameters.
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.
Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 72 (2005)
Issue (Month): 2 (April)
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral 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: (Zhang, Lei).
If references are entirely missing, you can add them using this form.