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Gamma-Minimax Prediction for the Multinomial Distribution

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Abstract

Characterizations of gamma-minimax predictors for the linear combinations of the unknown parameter and the random variable having the multinomial distribution under arbitrary squared error loss are established in two situations – when the sample size is fixed and when the sample size is a realization of a random variable. It is always assumed that the available vague prior information about the unknown parameter can be described by a class of priors whose vector of first moments belongs to a suitable convex and compact set. Several known gamma-minimax and minimax results can be obtained from the characterizations derived in the present paper.

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Authors and Affiliations

  1. Institute of Mathematics and Computer Science, Wrocław University of Technology, Wrocław, Poland

    Alicja Jokiel-Rokita

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  1. Alicja Jokiel-Rokita
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Correspondence to Alicja Jokiel-Rokita.

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Jokiel-Rokita, A. Gamma-Minimax Prediction for the Multinomial Distribution. Metrika 64, 259–269 (2006). https://doi.org/10.1007/s00184-006-0047-x

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  • DOI: https://doi.org/10.1007/s00184-006-0047-x

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