Nonparametric predictive inference and interval probability

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

doi:10.1016/j.jspi.2003.07.003

Nonparametric predictive inference and interval probability

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

Authors

T. Augustin

Professor Frank Coolen frank.coolen@durham.ac.uk
Professor

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.

Citation

Augustin, T., & Coolen, F. (2004). Nonparametric predictive inference and interval probability. Journal of Statistical Planning and Inference, 124(2), 251-272. https://doi.org/10.1016/j.jspi.2003.07.003

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.