Nonparametric Predictive Inference for Ordinal Data.

Coolen, F.P.A.; Coolen-Schrijner, P.; Coolen-Maturi, T.

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 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.

Citation

Coolen, F., Coolen-Schrijner, P., & Coolen-Maturi, T. (2013). Nonparametric Predictive Inference for Ordinal Data. Communications in Statistics - Theory and Methods, 42(19), 3478-3496. https://doi.org/10.1080/03610926.2011.632104

• Journal Article Type: Article
• Acceptance Date: Oct 11, 2011
• Online Publication Date: May 23, 2013
• Publication Date: 2013-10
• Deposit Date: Jan 24, 2014
• Journal: Communications in Statistics - Theory and Methods
• Print ISSN: 0361-0926
• Electronic ISSN: 1532-415X
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 42
• Issue: 19
• Pages: 3478-3496
• DOI: https://doi.org/10.1080/03610926.2011.632104
• Public URL: https://durham-repository.worktribe.com/output/1464300