Let x be a random variable such that, given θ, x is Poisson with mean θ, while θ has an unknown prior distribution G . In many statistical problems one wants to estimate as accurately as possible the parameter E (θǀ x = a ) for some given a = 0,1,.... If one assumes that G is a Gamma prior with unknown parameters α and β, then the problem is straightforward, but the estimate may not be consistent if G is not Gamma. On the other hand, a more general empirical Bayes estimator will always be consistent but will be inefficient if in fact G is Gamma. It is shown that this dilemma can be more or less resolved for large samples by combining the two methods of estimation.
CITATION STYLE
Robbins, H. (1980). An empirical Bayes estimation problem. Proceedings of the National Academy of Sciences, 77(12), 6988–6989. https://doi.org/10.1073/pnas.77.12.6988
Mendeley helps you to discover research relevant for your work.