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.
Quadratic-mean differentiability at a parameter
Definition
Let be a parametric model, let be open in , and let a supplied sigma-finite measure dominate every (Parametric statistical models, Dominated statistical experiments). Supply as part of the setup a family of nonnegative finite-valued measurable functions satisfying for every (The measure with density relative to ). Thus each integrates to one. The density family is supplied, rather than selected from domination alone; no choice principle, joint measurability in , or common exceptional null set is asserted. A supplied extended-valued probability density can be made finite-valued by setting it to zero on its measurable infinite-value set, which has measure zero because its integral is one. The QMD condition is unchanged by replacing any supplied density by an almost-everywhere equal version: for each fixed , the norm in the condition below changes only on the union of two null sets. At an interior point , the model is quadratic-mean differentiable (QMD) with score if is measurable, , and
as with . Values of the score on do not affect this condition. The Fisher information associated with this QMD score is ; it may be singular. QMD is an condition and does not assert pointwise differentiability of or local mutual absolute continuity of and .
Depends on
Used by
- The QMD route uses normalized absolutely continuous likelihoods and a separate uniform LAN premise Corollary
- A boundary null has a nonstandard likelihood-ratio limit Counterexample
- The uniform endpoint MLE has rate n, not square-root n Counterexample
- Wald, score, and likelihood-ratio statistics for regular nulls Definition
- A one-sided normal likelihood ratio at the boundary Example
- Gaussian shift LAN and the regular sample mean Example
- Known-variance normal mean and exact likelihood calibration Example
- A finite-dimensional QMD score has mean zero Lemma
- A QMD score is centered and has finite information Lemma
- Normalized product likelihood expansion under QMD Lemma
- QMD controls singular and zero-overlap masses Lemma
- Asymptotic linearity and normality of a regular MLE Theorem
- Convolution bound for general regular estimators Theorem
- Likelihood-ratio limit for local cones in a QMD model Theorem
- Quadratic-mean differentiability implies iid LAN Theorem
- Wald, score, and likelihood-ratio asymptotic chi-square calibration Theorem
Dependency tree · two levels
8 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
- Peter Bartlett, Theoretical Statistics, Stat 210B Lecture 22, PDF pp. 8–10 (standard reference, not scraped)
- Yen-Chi Chen, STAT 581 Chapter 3: M-estimation and the maximum likelihood estimator, QMD section (standard reference, not scraped)