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An open action set need not contain a posterior-risk minimizer

Statement refuted

Every posterior expected-loss function on a nonempty open action space attains its infimum.

Facts & Assumptions

Given: The singleton prior, singleton observation, open action interval, and loss in the Counterexample.

[F1]

A probability measure on a singleton assigns mass one to its point (Probability measures and probability spaces).

[F2]

Risk is the nonnegative integral of loss (Loss functions and risk).

[F3]

A posterior is a regular conditional distribution (Regular conditional distribution).

[F4]

The Bayes-rule theorem assumes a measurable posterior minimizer is attained (A posterior-risk minimizer minimizes prior risk).

Counterexample

Given: A one-point parameter space Θ={0} with prior mass one, a one-point data space with sampling mass one, the open action interval D=(0,1), and loss L(0,a)=a.

1.1givenF1F2F3

The prior and data law are point masses on singleton standard-Borel spaces by [F1]; their joint law is the point mass on the singleton pair. Define the posterior to be the point mass at 0. The only conditioning events are empty or the whole singleton, and the conditional-law identity holds for both, so this is a regular conditional law by [F3]. Integrating L(0,a)=a against it gives posterior risk q(a)=a by [F2].

2.1F4step 1.1algebra∎

For every a∈(0,1), q(a)=a>0, while the sequence an=1/(n+1) lies in (0,1) and has q(an)→0. Thus inf⁡a∈(0,1)q(a)=0 and the infimum is not attained. The claimed universal minimizer does not exist, which refutes the statement and illustrates the explicit existence premise in [F4].

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