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.
Strict concavity and uniqueness of a finite stationary maximizer
Statement
Suppose the canonical-link GLM has an open convex parameter domain , full-column-rank design , common scale , and at every predictor vector in . For a fixed response vector whose component densities are positive throughout , is strictly concave on . It has at most one stationary point; if a stationary point exists, it is the unique global maximizer. This conclusion does not assert that a finite maximizer exists.
Facts & Assumptions
Given: The canonical-link GLM hypotheses in the Statement and a fixed response vector whose log likelihood is defined throughout .
Canonical-link score and Hessian: the Hessian is .
The mean value theorem, as the case of Cauchy's: for continuous on with and differentiable on there is with : if a function is continuous on a closed interval and differentiable in its interior, its endpoint difference is its derivative at an interior point times the interval length.
Proof
Fix distinct , put , and define for . Convexity keeps this segment in . By [F1], , because by full column rank and every displayed is positive. The mean value theorem [F2] applied to makes strictly decreasing. For any , another application of [F2] gives for some and for some ; strict decrease gives the first secant slope larger than the second, which rearranges to . Thus every nonconstant segment is strictly concave.
Two distinct stationary points would give on their connecting segment, contradicting strict decrease. If is stationary and , then and strict decrease gives for ; [F2] applied to yields for some . Thus any stationary point is the unique global maximizer when it exists.
Depends on
Used by
Dependency tree · two levels
13 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
- Philippe Rigollet, MIT 18.650 Statistics for Applications, Lecture 10: Generalized Linear Models, PDF pp. 32–33, 40–42 (standard reference, not scraped)
- Ryan Tibshirani, Generalized Linear Models, CMU Advanced Methods for Data Analysis, PDF pp. 4–5 (standard reference, not scraped)