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Nonparametric predictive inference and interval probability

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

Authors

T. Augustin



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.

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
DOI https://doi.org/10.1016/j.jspi.2003.07.003
Keywords A(n), Capacities, Conditioning, Consistency, Imprecise probabilities, Interval probability, Non-parametrics, Low structure inference, Predictive inference, Updating.
Public URL https://durham-repository.worktribe.com/output/1567641