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Multiagent estimators of an exponential mean

Constance van Eeden and James V. Zidek

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Some Bayesian agents must produce a joint estimator of the mean of an exponentially distributed random variable S from a sample of realizations S. Their priors may differ but they have the same utility function. For the case of two agents, the Pareto efficient boundary of the utility set generated by the class of all non-randomized linear estimation rules is explored in this paper. Conditions are given that make those rules G-complete within the class of non-randomized linear estimators, meaning that optimum non-random estimators can be found on the Pareto boundary thereby providing a basis for a meaningful consensus.

Chapter information

Dominique Fourdrinier, Éric Marchand and Andrew L. Rukhin, eds., Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman (Beachwood, Ohio, USA: Institute of Mathematical Statistics, 2012), 131-153

First available in Project Euclid: 14 March 2012

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62C10: Bayesian problems; characterization of Bayes procedures 62C15: Admissibility
Secondary: 62F10: Point estimation

conjugate utility exponential–mean estimation forecasting G–admissibility multiagent decision theory Pareto optimality

Copyright © 2012, Institute of Mathematical Statistics


van Eeden, Constance; Zidek, James V. Multiagent estimators of an exponential mean. Contemporary Developments in Bayesian Analysis and Statistical Decision Theory: A Festschrift for William E. Strawderman, 131--153, Institute of Mathematical Statistics, Beachwood, Ohio, USA, 2012. doi:10.1214/11-IMSCOLL810.

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