Based on observations from a multinomial data set, a new method is presented for selecting a single category or the smallest subset of categories, where the selection criterion is a minimally required lower probability that (at least) a specific number of future observations will belong to that category or subset of categories. The inferences about the future observations are made using an extension of Coolen and Augustin's nonparametric predictive inference (NPI) model to a situation with multiple future observations.
Keywords. imprecise probability, predictive inference, categorical data, selection
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Authors addresses:
Rebecca Baker
Department of Mathematical Sciences
Science Laboratories, South Road
University of Durham
Durham, DH1 3LE
England
Frank Coolen
Department of Mathematical Sciences
Science Laboratories, South Road
Durham, DH1 3LE,
England
E-mail addresses:
| Rebecca Baker | r.m.baker@durham.ac.uk |
| Frank Coolen | Frank.Coolen@durham.ac.uk |