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Bracketing entropy implies empirical-process asymptotic equicontinuity
Statement
For a class of measurable real functions under a probability measure , an envelope means a nonnegative measurable function with -almost surely for every . Cauchy–Schwarz Cauchy-Schwarz inequality for gives when .
Assume Countable Choice. Let be iid with law , and let be a nonempty pointwise-measurable class of measurable functions with a nonnegative envelope satisfying . Assume and
Use the empirical process and semimetric from Empirical, centered, and Rademacher processes and Donsker classes and asymptotic equicontinuity. Then is asymptotically equicontinuous in probability with respect to .
Facts & Assumptions
Given: Countable Choice; an iid sequence, pointwise-measurable class , square-integrable envelope , and the finite normalized bracketing entropy integral in the statement.
Countable Choice permits fixing a finite bracket family at each of countably many dyadic scales (The Axiom of Countable Choice ()).
Empirical, centered, and Rademacher processes defines , , , outer probability, and the countable separable-version convention.
Donsker classes and asymptotic equicontinuity: and asymptotic equicontinuity is the vanishing-in-probability condition for the supremum of over .
Bracketing maximal inequality for the empirical process: nested finite partitions with cell oscillation envelopes of dyadic 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.
Covering, packing, bracketing numbers, and metric entropy: an bracket has measurable endpoints ordered -almost surely and width ; counts such brackets of width at most .
Dominated convergence: pointwise convergence dominated by an integrable function implies convergence in .
Markov's inequality for random variables: for nonnegative and , .
Cauchy-Schwarz inequality for : on a probability space, for every .
Proof
Given: The objects and hypotheses in the statement.
Choose a countable pointwise-dense subclass . For , set . Every has and is bounded in absolute value by , with .
If and lie in brackets and of width at most , respectively, then lies in the bracket Its width is , where and ; Cauchy–Schwarz gives its norm at most . Thus .
Fix and choose with . For each , take a finite bracketing cover of at width and partition the class by assigning each function to the first bracket containing it. Refine these finite partitions cumulatively through level ; by [A1], the countable family of finite covers and their representatives can be selected. If is the unrefined level- count, step 1.2 gives for , and the refined count satisfies . Consequently, using for and summing the resulting geometric weights, for universal constants : the unrefined entropy terms at scale are bounded by the integrand on the adjacent dyadic interval by monotonicity of bracketing numbers, and cumulative refinement contributes only a geometric sum of earlier terms. The added contributes a geometric sum of order , which is also bounded by the displayed integral because its integrand is at least . On each refined cell , define and choose . Countability makes this pointwise supremum measurable; containment in a level- bracket gives -almost everywhere, so it has the required 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.
Apply [F2] to with envelope and this nested partition. Its coarse term is at most , its dyadic tail is bounded by the right-hand side of step 2.1, and its final envelope-tail term tends to zero as for this fixed : for each fixed , by [F4].
The entropy integral in the statement makes the integral in step 2.1 tend to zero with . It also gives for each fixed : if , monotonicity yields . Therefore step 3.1 implies .
By [F5], Markov's inequality converts the expected-supremum limit in step 4.1 into asymptotic equicontinuity for pairs in . For arbitrary , choose pointwise approximants . The common envelope gives convergence by [F4]; applying [F4] to and also gives convergence. Pointwise convergence gives and for each fixed finite sample, so and the empirical-process increment converges pointwise to the desired increment. Hence a pair with is a limit of pairs from with distance below . The outer-probability modulus over is therefore bounded by the countable-class modulus over at radius , proving the asserted asymptotic equicontinuity.
Depends on
- The Axiom of Countable Choice ($\mathrm{AC}_\omega$)
- Markov's inequality for random variables
- Donsker classes and asymptotic equicontinuity
- Empirical, centered, and Rademacher processes
- Covering, packing, bracketing numbers, and metric entropy
- Bracketing maximal inequality for the empirical process
- Dominated convergence
- Cauchy-Schwarz inequality for $L^2$
Used by
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Sources
- Jon A. Wellner, Empirical Processes: Theory and Applications, Chapter 1, §7, Theorems 7.4–7.6 (standard reference, not scraped)
- David Pollard, Empirical Processes: Theory and Applications, Section 10 (standard reference, not scraped)