Open Access
VOL. 8 | 2012 Multiagent estimators of an exponential mean
Constance van Eeden, James V. Zidek

Editor(s) Dominique Fourdrinier, Éric Marchand, Andrew L. Rukhin

Inst. Math. Stat. (IMS) Collect., 2012: 131-153 (2012) DOI: 10.1214/11-IMSCOLL810


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.


Published: 1 January 2012
First available in Project Euclid: 14 March 2012

zbMATH: 1326.62019
MathSciNet: MR3202508

Digital Object Identifier: 10.1214/11-IMSCOLL810

Primary: 62C10 , 62C15
Secondary: 62F10

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

Rights: Copyright © 2012, Institute of Mathematical Statistics

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