Coolen, Frank P.A. and Coolen-Schrijner, Pauline and Maturi, Tahani A. (2010) On nonparametric predictive inference for ordinal data. In: Hüllermeier, Eyke and Kruse, Rudolf and Hoffmann, Frank, eds. Computational Intelligence for Knowledge-Based Systems Design 13th International Conference on Information Processing and Management of Uncertainty. Lecture Notes in Computer Science . Springer, Berlin, Germany, pp. 188-197. ISBN 978-3-642-14048-8. E-ISBN 978-3-642-14049-5. (doi:10.1007/978-3-642-14049-5) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:28521)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: http://dx.doi.org/10.1007/978-3-642-14049-5 |
Abstract
Nonparametric predictive inference (NPI) is a powerful frequentist statistical framework based only on an exchangeability assumption for future and past observations, made possible by the use of lower and upper probabilities. In this paper, NPI is presented for ordinal data, which are categorical data with an ordering of the categories. The method uses a latent variable representation of the observations and categories on the real line. Lower and upper probabilities for events involving the next observation are presented, and briefly compared to NPI for non-ordered categorical data. As an example application the comparison of two groups of ordinal data is presented.
Item Type: | Book section |
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DOI/Identification number: | 10.1007/978-3-642-14049-5 |
Uncontrolled keywords: | categorical data; lower and upper probabilities; nonparametric predictive inference; ordinal data; pairwise comparison |
Subjects: |
H Social Sciences > HA Statistics Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics |
Divisions: | Divisions > Kent Business School - Division > Kent Business School (do not use) |
Depositing User: | Tahani Coolen-Maturi |
Date Deposited: | 03 Dec 2011 18:15 UTC |
Last Modified: | 05 Nov 2024 10:10 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/28521 (The current URI for this page, for reference purposes) |
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