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.
Measurable finite-group permutation setups
Definition
Let be a statistical experiment (Statistical experiments), let , and let be a finite group (Group and abelian group) acting on by a left action (Left group actions, transitive actions, and faithful actions). Require that each map is measurable from to itself and that is measurable (A measurable function between measurable spaces).
For , the null law is invariant under the action when for every and . Equivalently, and have the same law when . The setup defines the action and score whether or not this invariance condition holds; the validity theorem below assumes it for every null parameter.
The data space need not be finite, and the action need not be free. Group elements, including distinct elements that send to the same point, remain distinct transformations in every group average. On a finite data space with the full power-set sigma-algebra, if every null law is invariant, the setup specializes to Finite-group randomization setups.
Depends on
Used by
- Identity-corrected random-permutation Monte Carlo p-value Algorithm
- The uncorrected Monte Carlo permutation fraction can be zero and invalid Counterexample
- Full-orbit and tie-randomized permutation p-values Definition
- A tied two-sample Bernoulli permutation test Example
- Unequal assignment weights under the Fisher sharp null Example
- Exact finite-group permutation p-values and tests Theorem
- Validity of identity-corrected random-permutation Monte Carlo p-values Theorem
Dependency tree · two levels
15 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
- Jesse Hemerik and Jelle Goeman, Exact Testing with Random Permutations, §§2.1–2.2 and 3.1–3.4 (standard reference, not scraped)
- Eugene Katsevich, STAT 9610 Lecture Notes, §3.2.4.2 (standard reference, not scraped)