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.
Likelihood-ratio vector for a point-mass model
Example
Assume AC. Let with its discrete -field, , and . The vector statistic is minimal sufficient and injective.
Facts & Assumptions
Given: AC, the point-mass family, and the positive geometric weights.
The Axiom of Choice: AC is the axiom branch of the likelihood-ratio-vector theorem.
A countable mixture likelihood-ratio vector is minimal sufficient: the RN-density vector relative to a positive countable mixture is minimal sufficient.
Minimal sufficient statistics: minimality is interpreted modulo common model-null sets.
Proof
Since and exactly when , the density is . Therefore has coordinate equal to and all other coordinates zero.
The discrete metric on is complete and separable, and its Borel sigma-field is the full sigma-field. The weights sum to one, and step 1.1 gives finite nonnegative measurable density versions. Since every -atom has positive mass, the common -null set on which these versions are reset is empty. Thus all hypotheses of [F1], including AC [A1], hold, and that theorem proves minimal sufficiency. The vectors in step 1.1 are distinct for distinct , proving injectivity. Finally, a set null under every contains no , so the family-null ideal is trivial and the minimality comparison in [F2] is exact in this model.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
18 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)
- Jiahao Tian, Chapter 2: Sufficiency and Exponential Families (standard reference, not scraped)