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Integral Jensen inequality for finite-dimensional convex losses
Statement
Assume the Axiom of Choice and Countable Choice. Let and let be a nonempty closed convex subset of , with closedness in the Euclidean topology (Interior, closure, boundary, limit point, isolated point and dense subset of a metric space) and convexity as in A convex subset of contains every line segment between two of its points. Let be a probability measure on its Borel sets, and assume , where is the Euclidean norm from The Euclidean inner product on . Define the barycenter coordinatewise and assume . If is Borel measurable and convex, then Convexity for this possibly extended-valued uses the extended-functional convention of Convex and strictly convex functionals on a convex subset of a real vector space, including its endpoint convention for . On a convex restriction where is finite, this inequality reduces to the real-valued Euclidean convention of Convex and strictly convex functions on Euclidean convex sets, as used in the subgradient step of the proof. The barycenter is the vector of coordinate integrals.
Facts & Assumptions
Given: The statement's , , , and barycenter .
Assume the Axiom of Choice and Countable Choice, as required by the supporting-hyperplane and subgradient suppliers (The Axiom of Choice, The Axiom of Countable Choice ()).
A point outside a nonempty closed convex set is strictly separated from it by a hyperplane (A point outside a nonempty closed convex set is strictly separated from it).
A boundary point of a nonempty convex set has a supporting hyperplane (Every boundary point belonging to a nonempty Euclidean convex set has a supporting hyperplane).
The supplier statement gives common ordinary interior and boundary; its proof steps 2.1--3.1 also prove that a convex set and its closure have the same relative interior in their affine hull (A convex set and its closure have the same interior and boundary).
A finite convex function has a subgradient at every interior point of its domain (A convex function has a subgradient at every interior point of its domain); a subgradient satisfies the global supporting inequality of Subgradients and the subdifferential of a convex function.
A nonnegative measurable function with integral zero vanishes almost everywhere (A nonnegative measurable function has integral exactly when it vanishes almost everywhere).
Coordinate integrals are linear and give the barycenter, while the expectation of an integrable real function is the difference of its positive- and negative-part integrals (Expectation of a nonnegative or integrable random variable, The Lebesgue integral is linear on ).
The nonnegative integral is monotone and positively homogeneous, so a pointwise nonnegative simple minorant gives a lower bound on the integral (Monotonicity and nonnegative homogeneity of the nonnegative integral).
The nonnegative integral is additive on finite sums (Additivity of the nonnegative Lebesgue integral).
Proof
If , the conclusion holds because . Otherwise let . For every integer , the simple function is bounded above by , so [F7] gives . If , these lower bounds are unbounded as (and are already infinite if ), a contradiction. Thus has full -measure. Let be the support of , the set of points every relative open neighbourhood of which has positive measure. Since has a countable base inherited from rational balls in , the complement of is a countable union of open null basic sets, so . Every neighbourhood of a point of meets , hence is dense in .
Let be the closure of the set of finite convex combinations of points of . Then . If , [F1] gives a linear functional and a real with for all . The function is integrable by the first-moment assumption and is nonnegative on the full-measure support . Hence almost everywhere, so [F5] gives and . By [F6] and the coordinate definition of , this says , contradicting the strict separation. Thus .
If were on the relative boundary of , [F2], applied in its affine hull, would give a nonzero supporting normal there with for all . Set . The first-moment assumption makes integrable, and [F6] gives . Since on the full-measure support , its negative part vanishes almost everywhere; [F5] gives . The signed-integral definition in [F6] then gives , so [F5] implies almost everywhere and therefore almost everywhere. By continuity of and the definition of support, every point of must lie in the hyperplane : otherwise a neighborhood of a support point outside it would have positive measure on which . Its closed convex hull would lie in that hyperplane, contradicting that is nonzero in the direction space of its affine hull. Hence is in the relative interior of .
Put , where denotes finite convex combinations. The extended-functional convexity inequality makes finite on ; on these finite values it is the real-valued Euclidean convexity of Convex and strictly convex functions on Euclidean convex sets. Density from step 1.1 gives . By [F3], is in the relative interior of . If this affine hull has dimension zero, then is concentrated at and the claim is equality. Otherwise identify the affine hull isometrically with ; [F4] applied to gives a subgradient at , so for every .
Let and , defining on . The function is integrable because has finite nonnegative integral and is integrable by the first-moment assumption. The supporting inequality gives on , a full-measure set; thus almost everywhere. By [F5], , so [F6] gives . Linearity [F6] now yields . Also , with almost everywhere; [F5] and additivity [F8] give . The integrable function is likewise zero almost everywhere, so [F5] applied to its positive and negative parts and linearity [F6] give . Finally [F6] and the barycenter definition yield . This proves the claim in the finite-integral case; the infinite-integral case was handled in step 1.1.
Depends on
- The Axiom of Choice
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Convex and strictly convex functions on Euclidean convex sets
- Convex and strictly convex functionals on a convex subset of a real vector space
- A convex subset of $\mathbb{R}^m$ contains every line segment between two of its points
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Interior, closure, boundary, limit point, isolated point and dense subset of a metric space
- Expectation of a nonnegative or integrable random variable
- A point outside a nonempty closed convex set is strictly separated from it
- Every boundary point belonging to a nonempty Euclidean convex set has a supporting hyperplane
- A convex set and its closure have the same interior and boundary
- A convex function has a subgradient at every interior point of its domain
- Subgradients and the subdifferential of a convex function
- A nonnegative measurable function has integral $0$ exactly when it vanishes almost everywhere
- The Lebesgue integral is linear on $L^1(\mu)$
- Monotonicity and nonnegative homogeneity of the nonnegative integral
- Additivity of the nonnegative Lebesgue integral
Used by
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Sources
- Ryan Martin, Lecture Notes on Advanced Statistical Theory, §5.2, PDF pp. 102–104 (standard reference, not scraped)
- Peter Hoff, Statistical Decision Problems, §§4–5, PDF pp. 7–13 (standard reference, not scraped)