Consider a parametric statistical model,
P(d
x|
θ), and an improper prior distribution,
ν(d
θ), that together yield a (proper) formal posterior distribution,
Q(d
θ|
x). The prior is called
strongly admissible if the generalized Bayes estimator of every bounded function of
θ is admissible under squared error loss. Eaton [M.L. Eaton, A statistical diptych: Admissible inferences-recurrence of symmetric Markov chains, Annals of Statistics 20 (1992) 1147–1179] used the Blyth–Stein Lemma to develop a sufficient condition, call it
, for strong admissibility of
ν. Our main result says that, under mild regularity conditions, if
ν satisfies
and
g(
θ) is a bounded, non-negative function, then the
perturbed prior distribution g(
θ)
ν(d
θ) also satisfies
and is therefore strongly admissible. Our proof has three basic components: (i) Eaton's [M.L. Eaton, A statistical diptych: Admissible inferences-recurrence of symmetric Markov chains, Annals of Statistics 20 (1992) 1147–1179] result that the condition
is equivalent to the
local recurrence of the Markov chain whose transition function is
R(d
θ|
η)=∫
Q(d
θ|
x)
P(d
x|
η); (ii) a new result for general state space Markov chains giving conditions under which local recurrence is equivalent to recurrence; and (iii) a new generalization of Hobert and Robert's [J.P. Hobert, C.P. Robert, Eaton's Markov chain, its conjugate partner and
-admissibility, Annals of Statistics 27 (1999) 361–373] result that says Eaton's Markov chain is recurrent if and only if the chain with transition function
is recurrent. One important application of our results involves the construction of strongly admissible prior distributions for estimation problems with restricted parameter spaces.
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