Multiagent estimators of an exponential mean
Constance van Eeden, James V. Zidek · Institute of Mathematical Statistics collections · 2012
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.