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Baker, Rebecca M.; Coolen, Frank P. A.

Nonparametric predictive category selection for multinomial data. (English) Zbl 05902630

J. Stat. Theory Pract. 4, No. 3, 509-526 (2010).
Summary: A new method is presented for selecting a single category or the smallest subset of categories, based on observations from a multinomial data set, 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.

Cited in 2 Documents

MSC:

62-XX Statistics

Keywords:

categorical data; lower and upper probability; multinomial data; nonparametric predictive inference; selection
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References:

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