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.
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Coolen, F.P.A., Coolen-Schrijner, P., Maturi, T.A. (2010). On Nonparametric Predictive Inference for Ordinal Data. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Computational Intelligence for Knowledge-Based Systems Design. IPMU 2010. Lecture Notes in Computer Science(), vol 6178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_20
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DOI: https://doi.org/10.1007/978-3-642-14049-5_20
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