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Bracketing entropy implies empirical-process asymptotic equicontinuity

Statement

For a class H of measurable real functions under a probability measure Q, an envelope H means a nonnegative measurable function with ∣h∣≤H Q-almost surely for every h∈H. Cauchy–Schwarz Cauchy-Schwarz inequality for L2 gives QH≤∥H∥Q,2<∞ when H∈L2(Q).

Assume Countable Choice. Let X1,X2,… be iid with law P, and let F be a nonempty pointwise-measurable class of measurable functions with a nonnegative envelope F satisfying PF2<∞. Assume 0<∥F∥P,2<∞ and

∫01log⁡N[](ϵ∥F∥P,2,F,L2(P)) dϵ<∞.

Use the empirical process Gn and semimetric ρP from Empirical, centered, and Rademacher processes and Donsker classes and asymptotic equicontinuity. Then Gn is asymptotically equicontinuous in probability with respect to ρP.

Facts & Assumptions

Given: Countable Choice; an iid sequence, pointwise-measurable class F, square-integrable envelope F, and the finite normalized L2(P) bracketing entropy integral in the statement.

[A1]

Countable Choice permits fixing a finite bracket family at each of countably many dyadic scales (The Axiom of Countable Choice (ACω)).

[A2]

Empirical, centered, and Rademacher processes defines Pn, P, Gn, outer probability, and the countable separable-version convention.

[F1]

Donsker classes and asymptotic equicontinuity: ρP(f,g)={P[(f−g)−P(f−g)]2}1/2 and asymptotic equicontinuity is the vanishing-in-probability condition for the supremum of ∣Gn(f)−Gn(g)∣ over ρP(f,g)<δ.

[F2]

Bracketing maximal inequality for the empirical process: nested finite partitions with cell oscillation envelopes of dyadic L2(P) size bound the expected supremum by a coarse finite-class term, an envelope-tail term, and the sum of dyadic widths times square-root log cell counts. Its complete revised proof is in this batch.

[F3]

Covering, packing, bracketing numbers, and metric entropy: an L2(P) bracket has measurable endpoints ordered P-almost surely and width ∥u−l∥P,2; N[](r,F,L2(P)) counts such brackets of width at most r.

[F4]

Dominated convergence: pointwise convergence dominated by an integrable function implies convergence in L1.

[F5]

Markov's inequality for random variables: for nonnegative Y and r>0, P(Y>r)≤EY/r.

[F6]

Cauchy-Schwarz inequality for L2: on a probability space, P∣Y∣≤∥Y∥P,2 for every Y∈L2(P).

Proof

Given: The objects and hypotheses in the statement.

1.1A2F1F6givenconstructalgebra

Choose a countable pointwise-dense subclass F0⊆F. For δ>0, set Hδ={(f−g)−P(f−g):f,g∈F0, ρP(f,g)≤δ}. Every h∈Hδ has ∥h∥P,2≤δ and is bounded in absolute value by H:=2F+2PF, with ∥H∥P,2≤4∥F∥P,2.

1.2A2givenF1F3F6algebra

If f and g lie in L2(P) brackets [li,ui] and [lj,uj] of width at most r∥F∥P,2, respectively, then (f−g)−P(f−g) lies in the bracket [li−uj−P(ui−lj), ui−lj−P(li−uj)]. Its width is wi+wj+P(wi+wj), where wi=ui−li and wj=uj−lj; Cauchy–Schwarz gives its L2(P) norm at most 2(∥wi∥P,2+∥wj∥P,2)≤4r∥F∥P,2. Thus N[](4r∥F∥P,2,Hδ,L2(P))≤N[](r∥F∥P,2,F,L2(P))2.

2.1A1A2F1F2F3step 1.2algebra

Fix 0<δ<∥H∥P,2/2 and choose q0≥1 with δ/2<2−q0∥H∥P,2≤δ. For each q≥q0, take a finite bracketing cover of Hδ at width 2−q∥H∥P,2 and partition the class by assigning each function to the first bracket containing it. Refine these finite partitions cumulatively through level q; by [A1], the countable family of finite covers and their representatives can be selected. If bq is the unrefined level-q count, step 1.2 gives bq≤N[](rq∥F∥P,2,F,L2(P))2 for rq=2−q∥H∥P,2/(4∥F∥P,2), and the refined count satisfies mq≤∏p=q0qbp. Consequently, using 1+∑xp≤1+∑xp for xp≥0 and summing the resulting geometric weights, ∑q≥q02−q1+log⁡(2mq)≤C1∫0C2δ/∥F∥P,21+log⁡N[](ϵ∥F∥P,2,F,L2(P)) dϵ for universal constants C1,C2: the unrefined entropy terms at scale rq are bounded by the integrand on the adjacent dyadic interval [rq/2,rq] by monotonicity of bracketing numbers, and cumulative refinement contributes only a geometric sum of earlier terms. The added 1 contributes a geometric sum of order 2−q0, which is also bounded by the displayed integral because its integrand is at least 1. On each refined cell C, define Δq,C=sup⁡h,h′∈C∣h−h′∣ and choose πq(C)∈C. Countability makes this pointwise supremum measurable; containment in a level-q bracket gives Δq,C≤u−l P-almost everywhere, so it has the required L2(P) bound. Intersecting partitions makes these oscillations nonincreasing on child cells. Thus these partitions and widths satisfy the hypotheses of [F2] when the local maximal inequality is applied.

3.1A1F2F4step 2.1algebra

Apply [F2] to Hδ with envelope H and this nested partition. Its coarse term is at most Cδ1+log⁡(2mq0), its dyadic tail is bounded by the right-hand side of step 2.1, and its final envelope-tail term tends to zero as n→∞ for this fixed δ: for each fixed a>0, nP(H1{H>an})≤a−1P(H21{H>an})→0 by [F4].

4.1F1F3step 2.1step 3.1algebra

The entropy integral in the statement makes the integral in step 2.1 tend to zero with δ. It also gives δlog⁡N[](cδ∥F∥P,2,F,L2(P))→0 for each fixed c>0: if h(ϵ)=log⁡N[](ϵ∥F∥P,2,F,L2(P)), monotonicity yields (ϵ/2)h(ϵ)≤∫ϵ/2ϵh(u) du→0. Therefore step 3.1 implies lim⁡δ↓0lim sup⁡nEsup⁡h∈Hδ∣Gn(h)∣=0.

5.1A2F1F4F5step 4.1givenalgebra∎

By [F5], Markov's inequality converts the expected-supremum limit in step 4.1 into asymptotic equicontinuity for pairs in F0. For arbitrary f,g∈F, choose pointwise approximants fk,gk∈F0. The common envelope gives L1(P) convergence by [F4]; applying [F4] to ∣fk−f∣2≤4F2 and ∣gk−g∣2≤4F2 also gives L2(P) convergence. Pointwise convergence gives Pnfk→Pnf and Pngk→Png for each fixed finite sample, so ρP(fk,gk)→ρP(f,g) and the empirical-process increment converges pointwise to the desired increment. Hence a pair with ρP(f,g)<δ/2 is a limit of pairs from F0 with distance below δ. The outer-probability modulus over F is therefore bounded by the countable-class modulus over F0 at radius δ, proving the asserted asymptotic equicontinuity.

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