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Maximal Inequalities for Empirical Processes under General Mixing Conditions with an Application to Strong Approximations

Demian Pouzo

arXiv 17 Feb 2024 · Mathematics — Probability · publishedStochastic Processes and their Applications (2026)

arXiv:2402.11394 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper provides a bound for the supremum of sample averages over a class of functions for a general class of mixing stochastic processes with arbitrary mixing rates. Regardless of the speed of mixing, the bound is comprised of a concentration rate and a novel measure of complexity. The speed of mixing, however, affects the former quantity implying a phase transition. Fast mixing leads to the standard root-n concentration rate, while slow mixing leads to a slower concentration rate, its speed depends on the mixing structure. Our findings are applied to derive strong approximation results for a general class of mixing processes with arbitrary mixing rates.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Talagrand, Michel (2014) Upper and Lower Bounds for Stochastic Processes0.95616488%
2Doukhan, Paul and Massart, Pascal and Rio, Emmanuel (1995) Invariance principles for absolutely regular empirical processes0.95315487%
3Rio, Emmanuel (2017) Asymptotic theory of weakly dependent random processes0.8746367%
4Dedecker, Jérôme and Prieur, Clémentine (2005) New dependence coefficients. Examples and applications to statistics0.86011464%
5Dedecker, Jérôme and Prieur, Clémentine (2004) Coupling for $ $-dependent sequences and applications0.86011364%
6Dedecker, Jérôme and Merlevede, Florence (2006) Inequalities for partial sums of Hilbert-valued dependent sequences and applications0.69312250%
7Ramon van Handel (2018) Chaining, interpolation and convexity II: The contraction principle0.6443267%
8van der Vaart, Aad W. and Wellner, Jon (1996) Weak Convergence and Empirical Processes with Applications to Statistics0.6443267%
9Talagrand, Michel (2005) The Generic Chaining0.64422100%
10Yu, Bin (1994) Rates of convergence for empirical processes of stationary mixing sequences0.64422100%

Showing the top 10 of 25 scored citations.