Pith. sign in

REVIEW 4 cited by

A short proof of the Dvoretzky--Kiefer--Wolfowitz--Massart inequality

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2403.16651 v1 pith:LXR2BSTU submitted 2024-03-25 math.PR math.STstat.TH

A short proof of the Dvoretzky--Kiefer--Wolfowitz--Massart inequality

classification math.PR math.STstat.TH
keywords bounddistributiondvoretzky--kiefer--wolfowitz--massartfunctionholdsinequalitypopulationproof
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
read the original abstract

The Dvoretzky--Kiefer--Wolfowitz--Massart inequality gives a sub-Gaussian tail bound on the supremum norm distance between the empirical distribution function of a random sample and its population counterpart. We provide a short proof of a result that improves the existing bound in two respects. First, our one-sided bound holds without any restrictions on the failure probability, thereby verifying a conjecture of Birnbaum and McCarty (1958). Second, it is local in the sense that it holds uniformly over sub-intervals of the real line with an error rate that adapts to the behaviour of the population distribution function on the interval.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. A data-dependent DKW inequality for regenerative Markov chains

    math.ST 2026-06 unverdicted novelty 7.0

    Establishes an empirical concentration inequality for the empirical CDF of a functional on regenerative Markov chains, with data-dependent leading term and lower-order convergence bound.

  2. Estimation beyond Missing (Completely) at Random

    math.ST 2024-10 unverdicted novelty 7.0

    Realisable epsilon-contamination models for MNAR data yield minimax mean estimation rates that decompose into MCAR plus robust terms and remain consistent for Gaussian bases even as missingness and epsilon both tend to 1.

  3. A data-dependent DKW inequality for regenerative Markov chains

    math.ST 2026-06 accept novelty 6.0

    An explicit nearly-optimal data-dependent DKW inequality yields uniform 1-δ confidence bands for the CDF of a functional of a regenerative Markov chain from its empirical CDF.

  4. An Elementary Proof of the Dvoretzky--Kiefer--Wolfowitz--Massart Inequality

    math.PR 2026-07 accept novelty 5.0

    A discrete-martingale plus Sion-minimax argument proves P(sup(ˆF_n−F)>ε)≤exp(−2nε²) for every ε>0, recovering the classical DKW–Massart bound.