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.
A discrete pivot can give conservative coverage
Example
Let , let the measurable observation space be , and for each assign probability to each of . Let have this law, put , choose nominal level , and use the central pivot set . The inverted set is Its coverage is , so it is conservative for the nominal level. The atom masses prevent the equal-tail quantiles from calibrating the central acceptance set to probability .
Facts & Assumptions
Given: The finite model and the procedure specified in the Example.
Probability measures and probability spaces: a probability measure has total mass one.
Pivotal quantities: a pivot has a parameter-free sampling law.
Confidence sets and coverage: coverage is the probability that the true parameter belongs to the procedure's output.
Coverage from pivot inversion and exact calibration: pivot inversion has at-least coverage when the acceptance set has probability at least the target level.
Sigma-algebras: a sigma-algebra is an algebra of subsets closed under countable unions.
Statistical experiments: an experiment fixes a measurable observation space and a probability law for each parameter.
Verification
The full power set is an algebra of subsets closed under countable unions, so it is a sigma-algebra by [F5]. Explicitly set for every . These values are nonnegative and have total mass . For a pairwise disjoint sequence , . Thus it is countably additive, hence a probability measure, and specifies the experiment family in [F6]. Under , assigns mass to each of , independently of ; hence is a pivot with .
Define and the generalized quantile . By the definition of , the event is , which occurs for all three possible observations. Thus its probability and coverage equal ; this exceeds , while [F4] also applies since . The pivot cdf is below , on , on , and from onward. Thus the generalized and quantiles are and , and their closed interval contains all atoms.
Depends on
Used by
Nothing in the library uses this result yet.
Dependency tree · two levels
16 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
- Karl B. Gregory, USC STAT 713, Lecture 14: Confidence Intervals and Sets, slide 9, PDF p. 14 (standard reference, not scraped)