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Common-reference factorization criterion for sufficiency

Statement

Let (X,A) and (Y,B) be standard Borel, let Θ≠∅, and let T:X→Y be a statistic. Fix θ0∈Θ and set P0=Pθ0. Assume Pθ≪P0 for every θ. Then T is sufficient if and only if for every θ the Radon–Nikodym derivative dPθ/dP0 has a σ(T)-measurable version. Equivalently, for each θ there is a Borel gθ:Y→[0,∞) with dPθ/dP0=gθ(T) P0-almost surely. The null exceptional set may depend on θ. The requirement P0∈{Pθ} is essential; an arbitrary external dominating probability is not covered.

Facts & Assumptions

Given: the standard Borel spaces, T, the model, the in-model reference P0, and absolute continuity.

[A1]

The Axiom of Choice: the Radon–Nikodym theorem is used under AC.

[F1]

Sufficient statistics: sufficiency is the existence of one conditional distribution kernel common to all model laws.

[F8]

Statistics and estimators: T is a measurable data map, so its generated sigma-field and inverse images are measurable.

[F2]

Measure kernel and probability kernel: kernel evaluations are measurable, and each fixed conditioning value gives a probability measure.

[F3]

Existence of regular conditional distributions for standard borel targets and Regular conditional kernels factor through a standard borel conditioning variable: under the standard Borel hypotheses, a regular conditional distribution given σ(T) factors through T to a probability kernel on the target.

[F4]

A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density: an absolutely continuous probability has an RN density, unique almost everywhere, and integration against it recovers the probability.

[F5]

Likelihood-ratio change of measure: under absolute continuity, integrating against the RN derivative changes P0 to Pθ.

[F6]

Doob-Dynkin factorization through the sigma-algebra generated by a function: a real σ(T)-measurable function factors through a Borel function of a real-valued statistic.

[F7]

Standard borel spaces admit bimeasurable real codings: a standard Borel target is Borel-isomorphic to a Borel subset of R, so the Doob–Dynkin factor extends by a fixed value off that subset.

[F9]

Every nonnegative measurable function admits an explicit increasing sequence of simple approximations and Monotone convergence for the integral: identities for indicators under a regular conditional distribution extend to nonnegative measurable weights by increasing simple approximations and monotone convergence.

Proof

1.1A1F1F4F5F8F9givenconstruct

Suppose T is sufficient, and let K be its common kernel. Fix θ. Disintegrating Pθ and P0 with this same kernel gives Pθ(A)=∫K(y,A)PθT(dy) and P0(A)=∫K(y,A)P0T(dy) for every A. Since Pθ≪P0, also PθT≪P0T; let rθ=dPθT/dP0T. Change of measure [F5] gives Pθ(A)=∫K(y,A)rθ(y)P0T(dy). The disintegration identity under P0 extends to the nonnegative weight rθ by [F9], making this ∫Arθ(T) dP0. Uniqueness in [F4] gives dPθ/dP0=rθ(T) P0-almost surely.

2.1F6F7step 1.1given

This proves that the derivative has a σ(T)-measurable version. Modify that version on the σ(T)-measurable null set where it is negative or infinite to obtain a finite nonnegative version. By [F6, F7] this version has a Borel factor gθ(T) on the standard Borel target; reset target values where gθ is negative or infinite to zero, which preserves the identity P0-almost surely.

3.1F1F2F3F5F8F9givenalgebra∎

Conversely suppose dPθ/dP0=gθ(T) P0-almost surely for every θ. By [F3], a regular conditional distribution under P0 factors through T to a kernel K0 on Y. Change of measure [F5] shows PθT(C)=∫Cgθ(y)P0T(dy). For every A and Borel C, Pθ(A∩T−1C)=∫1A1C(T)gθ(T)dP0=∫Cgθ(y)K0(y,A)P0T(dy)=∫CK0(y,A)PθT(dy). The weighted conditional identity follows from [F9]. Thus the same K0 is a conditional law for every parameter, and [F1] gives sufficiency.

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