How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Common-reference factorization criterion for sufficiency
Statement
Let and be standard Borel, let , and let be a statistic. Fix and set . Assume for every . Then is sufficient if and only if for every the Radon–Nikodym derivative has a -measurable version. Equivalently, for each there is a Borel with -almost surely. The null exceptional set may depend on . The requirement is essential; an arbitrary external dominating probability is not covered.
Facts & Assumptions
Given: the standard Borel spaces, , the model, the in-model reference , and absolute continuity.
The Axiom of Choice: the Radon–Nikodym theorem is used under AC.
Sufficient statistics: sufficiency is the existence of one conditional distribution kernel common to all model laws.
Statistics and estimators: is a measurable data map, so its generated sigma-field and inverse images are measurable.
Measure kernel and probability kernel: kernel evaluations are measurable, and each fixed conditioning value gives a probability measure.
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 factors through to a probability kernel on the target.
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.
Likelihood-ratio change of measure: under absolute continuity, integrating against the RN derivative changes to .
Doob-Dynkin factorization through the sigma-algebra generated by a function: a real -measurable function factors through a Borel function of a real-valued statistic.
Standard borel spaces admit bimeasurable real codings: a standard Borel target is Borel-isomorphic to a Borel subset of , so the Doob–Dynkin factor extends by a fixed value off that subset.
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
Suppose is sufficient, and let be its common kernel. Fix . Disintegrating and with this same kernel gives and for every . Since , also ; let . Change of measure [F5] gives . The disintegration identity under extends to the nonnegative weight by [F9], making this . Uniqueness in [F4] gives -almost surely.
This proves that the derivative has a -measurable version. Modify that version on the -measurable null set where it is negative or infinite to obtain a finite nonnegative version. By [F6, F7] this version has a Borel factor on the standard Borel target; reset target values where is negative or infinite to zero, which preserves the identity -almost surely.
Conversely suppose -almost surely for every . By [F3], a regular conditional distribution under factors through to a kernel on . Change of measure [F5] shows . For every and Borel , . The weighted conditional identity follows from [F9]. Thus the same is a conditional law for every parameter, and [F1] gives sufficiency.
Depends on
- Sufficient statistics
- Statistics and estimators
- Standard Borel spaces
- Measure kernel and probability kernel
- Regular conditional distribution
- Existence of regular conditional distributions for standard borel targets
- Regular conditional kernels factor through a standard borel conditioning variable
- A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density
- Likelihood-ratio change of measure
- Doob-Dynkin factorization through the sigma-algebra generated by a function
- Standard borel spaces admit bimeasurable real codings
- The Axiom of Choice
- Every nonnegative measurable function admits an explicit increasing sequence of simple approximations
- Monotone convergence for the integral
Used by
Dependency tree · two levels
72 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Ryan Martin, Lecture Notes on Advanced Statistical Theory (standard reference, not scraped)
- Joshua M. Tebbs, STAT 512 Mathematical Statistics Lecture Notes (standard reference, not scraped)
- Peter Kempthorne, MIT 18.655 Lecture Note 6: Sufficiency (standard reference, not scraped)
- Noah Fithian, Stat 210a Reader: Sufficiency (standard reference, not scraped)
- Noah Fithian, Stat 210a Reader: Completeness, Ancillarity, and Basu’s Theorem (standard reference, not scraped)
- Charles J. Geyer, Stat 5421 Lecture Notes: Exponential Families (standard reference, not scraped)
- Jiahao Tian, Chapter 2: Sufficiency and Exponential Families (standard reference, not scraped)