Nonparametric Predictive Inference for Ordinal Data

Coolen, Frank P. A. and Coolen-Maturi, Tahani and Coolen-Schrijner, Pauline and Elkhafifi, F.F.G.A (2013) Nonparametric Predictive Inference for Ordinal Data. Communications in Statistics-Theory and Methods, 42 (19). pp. 3478-3496. ISSN 0361-0926. (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)

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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 article, 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 application, the comparison of multiple groups of ordinal data is presented.

Item Type: Article
Uncontrolled keywords: Categorical data, Lower and upper probabilities, Multiple comparisons, Nonparametric predictive inference, Ordinal data
Subjects: H Social Sciences > H Social Sciences (General)
Divisions: Faculties > Social Sciences > Kent Business School > Management Science
Depositing User: Karen Finch
Date Deposited: 19 Jan 2012 12:29
Last Modified: 23 Apr 2014 09:20
Resource URI: (The current URI for this page, for reference purposes)
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