Cookies

We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.


Durham Research Online
You are in:

Nonparametric predictive inference and interval probability.

Augustin, T. and Coolen, F. P. A. (2004) 'Nonparametric predictive inference and interval probability.', Journal of statistical planning and inference., 124 (2). pp. 251-272.

Abstract

The assumption A(n), proposed by Hill (J. Amer. Statist. Assoc. 63 (1968) 677), provides a natural basis for low structure non-parametric predictive inference, and has been justified in the Bayesian framework. This paper embeds A(n)-based inference into the theory of interval probability, by showing that the corresponding bounds are totally monotone F-probability and coherent. Similar attractive internal consistency results are proven to hold for conditioning and updating.

Item Type:Article
Keywords:A(n), Capacities, Conditioning, Consistency, Imprecise probabilities, Interval probability, Non-parametrics, Low structure inference, Predictive inference, Updating.
Full text:Full text not available from this repository.
Publisher Web site:http://dx.doi.org/10.1016/j.jspi.2003.07.003
Record Created:26 Apr 2007
Last Modified:31 Mar 2010 12:08

Social bookmarking: del.icio.usConnoteaBibSonomyCiteULikeFacebookTwitterExport: EndNote, Zotero | BibTex
Usage statisticsLook up in GoogleScholar | Find in a UK Library