RKL: a general, invariant Bayes solution for Neyman-Scott
This provides a consistent solution for a classic statistical estimation problem, addressing limitations of past ad-hoc or non-invariant approaches.
The paper tackles the Neyman-Scott estimation problem, where standard methods often yield inconsistent results, by introducing a general-purpose Bayes estimator that is invariant to representation and achieves consistency over any non-degenerate prior.
Neyman-Scott is a classic example of an estimation problem with a partially-consistent posterior, for which standard estimation methods tend to produce inconsistent results. Past attempts to create consistent estimators for Neyman-Scott have led to ad-hoc solutions, to estimators that do not satisfy representation invariance, to restrictions over the choice of prior and more. We present a simple construction for a general-purpose Bayes estimator, invariant to representation, which satisfies consistency on Neyman-Scott over any non-degenerate prior. We argue that the good attributes of the estimator are due to its intrinsic properties, and generalise beyond Neyman-Scott as well.