English

On Optimal Solutions to Compound Statistical Decision Problems

Statistics Theory 2019-12-02 v2 Statistics Theory

Abstract

In a compound decision problem, consisting of nn statistically independent copies of the same problem to be solved under the sum of the individual losses, any reasonable compound decision rule δ\delta satisfies a natural symmetry property, entailing that δ(σ(y))=σ(δ(y))\delta(\sigma(\boldsymbol{y})) = \sigma(\delta(\boldsymbol{y})) for any permutation σ\sigma. We derive the greatest lower bound on the risk of any such decision rule. The classical problem of estimating the mean of a homoscedastic normal vector is used to demonstrate the theory, but important extensions are presented as well in the context of Robbins's original ideas.

Keywords

Cite

@article{arxiv.1911.11422,
  title  = {On Optimal Solutions to Compound Statistical Decision Problems},
  author = {Asaf Weinstein},
  journal= {arXiv preprint arXiv:1911.11422},
  year   = {2019}
}

Comments

The main result is, apparently, already known. See, e.g., Berger (1985, Ch. 6), Greenshtein, E. & Ritov (2009)

R2 v1 2026-06-23T12:27:25.544Z