Open Access
VOL. 8 | 2012 Asymptotic admissibility of priors and elliptic differential equations
John A. Hartigan

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

Inst. Math. Stat. (IMS) Collect., 2012: 117-130 (2012) DOI: 10.1214/11-IMSCOLL809


We evaluate priors by the second order asymptotic behaviour of the corresponding estimators. Under certain regularity conditions, the risk differences between efficient estimators of parameters taking values in a domain D, an open connected subset of Rd, are asymptotically expressed as elliptic differential forms depending on the asymptotic covariance matrix V. Each efficient estimator has the same asymptotic risk as a “local Bayes” estimate corresponding to a prior density p. The asymptotic decision theory of the estimators identifies the smooth prior densities as admissible or inadmissible, according to the existence of solutions to certain elliptic differential equations. The prior p is admissible if the quantity pV is sufficiently small near the boundary of D. We exhibit the unique admissible invariant prior for V=I, D=Rd{0}. A detailed example is given for a normal mixture model.


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

zbMATH: 1326.62020
MathSciNet: MR3202507

Digital Object Identifier: 10.1214/11-IMSCOLL809

Primary: 62C20
Secondary: 62F10 , 62P30

Keywords: Birge ratio , consensus value , jackknife estimator , matrix weighted means , Meta-analysis , normal mean , shrinkage estimator

Rights: Copyright © 2012, Institute of Mathematical Statistics

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