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Nonparametric predictive inference and interval probability

Augustin, T.; Coolen, F.P.A.


T. Augustin


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.


Augustin, T., & Coolen, F. (2004). Nonparametric predictive inference and interval probability. Journal of Statistical Planning and Inference, 124(2), 251-272.

Journal Article Type Article
Publication Date Sep 1, 2004
Deposit Date Apr 26, 2007
Journal Journal of Statistical Planning and Inference
Print ISSN 0378-3758
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 124
Issue 2
Pages 251-272
Keywords A(n), Capacities, Conditioning, Consistency, Imprecise probabilities, Interval probability, Non-parametrics, Low structure inference, Predictive inference, Updating.