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Strict concavity and uniqueness of a finite stationary maximizer

Statement

Suppose the canonical-link GLM has an open convex parameter domain Θ⊆Rp, full-column-rank design X, common scale a(ϕ)>0, and b′′(ηi)>0 at every predictor vector in XΘ. For a fixed response vector y whose component densities are positive throughout Θ, ℓ( ⋅ ;y,ϕ) is strictly concave on Θ. 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 Θ.

[F1]

Canonical-link score and Hessian: the Hessian is −XTdiag⁡(b′′(ηi))X/a(ϕ).

[F2]

The mean value theorem, as the case g(x)=x of Cauchy's: for f continuous on [a,b] with a<b and differentiable on (a,b) there is c∈(a,b) with f(b)−f(a)=f′(c)(b−a): 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

1.1F1F2givenalgebra

Fix distinct β0,β1∈Θ, put d=β1−β0, and define q(t)=ℓ(β0+td;y,ϕ) for 0≤t≤1. Convexity keeps this segment in Θ. By [F1], q′′(t)=dT∇2ℓ(β0+td)d=−a(ϕ)−1∑ib′′(xiT(β0+td))(xiTd)2<0, because Xd≠0 by full column rank and every displayed b′′ is positive. The mean value theorem [F2] applied to q′ makes q′ strictly decreasing. For any 0<t<1, another application of [F2] gives (q(t)−q(0))/t=q′(u) for some u∈(0,t) and (q(1)−q(t))/(1−t)=q′(v) for some v∈(t,1); strict decrease gives the first secant slope larger than the second, which rearranges to q(t)>(1−t)q(0)+tq(1). Thus every nonconstant segment is strictly concave.

2.1F2step 1.1algebra∎

Two distinct stationary points would give q′(0)=q′(1)=0 on their connecting segment, contradicting strict decrease. If β0 is stationary and β1≠β0, then q′(0)=0 and strict decrease gives q′(t)<0 for 0<t≤1; [F2] applied to q yields ℓ(β1;y,ϕ)−ℓ(β0;y,ϕ)=q′(c)<0 for some c∈(0,1). Thus any stationary point is the unique global maximizer when it exists.

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