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Let $T$ be a general sampling statistic that can be written as a linear statistic plus an error term. Uniform and non-uniform Berry--Esseen type bounds for $T$ are obtained. The bounds are the best possible for many known statistics.…

Statistics Theory · Mathematics 2009-09-29 Louis H. Y. Chen , Qi-Man Shao

We derive concentration inequalities for maxima of empirical processes associated with Poisson point processes. The proofs are based on a careful application of Ledoux's entropy method. We demonstrate the utility of the obtained…

Probability · Mathematics 2018-07-19 Martin Kroll

Concentration bounds for non-product, non-Haar measures are fairly recent: the first such result was obtained for contracting Markov chains by Marton in 1996 via the coupling method. The work that followed, with few exceptions, also used…

Probability · Mathematics 2012-07-10 Leonid , Kontorovich

Limit distributions for the greatest convex minorant and its derivative are considered for a general class of stochastic processes including partial sum processes and empirical processes, for independent, weakly dependent and long range…

Statistics Theory · Mathematics 2016-08-16 D. Anevski , O. Hössjer

The asymptotic behaviour of empirical measures has been studied extensively. In this paper, we consider empirical measures of given subordinated processes on complete (not necessarily compact) and connected Riemannian manifolds with…

Probability · Mathematics 2022-08-17 Huaiqian Li , Bingyao Wu

We perform the apriori analysis of solutions to critical nonlinear elliptic equations on manifolds with boundary. The solutions are of minimizing type. The originality is that we impose no condition on the boundary, which leads us to assume…

Analysis of PDEs · Mathematics 2025-01-09 Hussein Mesmar , Frédéric Robert

Ferguson's Dirichlet process plays an important role in nonparametric Bayesian inference. Let $P_a$ be the Dirichlet process in $\mathbb{R}$ with a base probability measure $H$ and a concentration parameter $a>0.$ In this paper, we show…

Statistics Theory · Mathematics 2011-12-15 Luai Al Labadi , Mahmoud Zarepour

We present a tail inequality for suprema of empirical processes generated by variables with finite $\psi_\alpha$ norms and apply it to some geometrically ergodic Markov chains to derive similar estimates for empirical processes of such…

Probability · Mathematics 2008-06-08 Radosław Adamczak

This note describes non-asymptotic variance and tail bounds for order statistics of samples of independent identically distributed random variables. Those bounds are checked to be asymptotically tight when the sampling distribution belongs…

Probability · Mathematics 2012-11-05 Stephane Boucheron , Maud Thomas

In this article we establish exponential moment bounds, moment bounds in fractional order smoothness spaces, a uniform H\"older continuity in time, and strong convergence rates for a class of fully discrete exponential Euler-type numerical…

Probability · Mathematics 2021-11-02 Arnulf Jentzen , Felix Lindner , Primož Pušnik

We present a positive solution to the so-called Bernoulli Conjecture concerning the characterization of sample boundedness of Bernoulli processes. We also discuss some applications and related open problems.

Probability · Mathematics 2014-09-19 Witold Bednorz , Rafał Latała

For an indeterminate Hamburger moment problem we consider an infinite family of analytic densities solving the moment problem and we prove that they all have finite (Shannon) entropy. These densities are either all bounded or all unbounded.…

Probability · Mathematics 2025-12-23 Christian Berg

The Heisenberg limit provides a fundamental bound on the achievable estimation precision with a limited number of $N$ resources used (e.g., atoms, photons, etc.). Using entangled quantum states makes it possible to scale the precision with…

Quantum Physics · Physics 2023-04-28 Wojciech Górecki

We obtain optimal moment bounds for Birkhoff sums, and optimal concentration inequalities, for a large class of slowly mixing dynamical systems, including those that admit anomalous diffusion in the form of a stable law or a central limit…

Dynamical Systems · Mathematics 2017-09-01 Sébastien Gouëzel , Ian Melbourne

Polyak-Ruppert averaging is a widely used technique to achieve the optimal asymptotic variance of stochastic approximation (SA) algorithms, yet its high-probability performance guarantees remain underexplored in general settings. In this…

Machine Learning · Statistics 2025-05-29 Sajad Khodadadian , Martin Zubeldia

This paper is mainly concerned with the existence of extremals for the Adams inequality. We first establish an upper bound for the classical Adams functional along of all concentrated sequences in $W^{m,\frac{n}{m}}_{\mathcal{N}}(\Omega)$,…

Analysis of PDEs · Mathematics 2022-07-26 José Francisco Alves de Oliveira , Abiel Costa Macedo

We derive novel anti-concentration bounds for the difference between the maximal values of two Gaussian random vectors across various settings. Our bounds are dimension-free, scaling with the dimension of the Gaussian vectors only through…

Statistics Theory · Mathematics 2024-08-27 Alexandre Belloni , Ethan X. Fang , Shuting Shen

We characterize the value function and the optimal stopping time for a large class of optimal stopping problems where the underlying process to be stopped is a fairly general Markov process. The main result is inspired by recent findings…

Probability · Mathematics 2012-04-03 Sören Christensen , Paavo Salminen , Bao Quoc Ta

We consider the convergence of empirical processes indexed by functions that depend on an estimated parameter $\eta$ and give several alternative conditions under which the ``estimated parameter'' $\eta_n$ can be replaced by its natural…

Statistics Theory · Mathematics 2007-09-12 Aad W. van der Vaart , Jon A. Wellner

We prove several different anti-concentration inequalities for functions of independent Bernoulli-distributed random variables. First, motivated by a conjecture of Alon, Hefetz, Krivelevich and Tyomkyn, we prove some "Poisson-type"…

Combinatorics · Mathematics 2023-06-22 Jacob Fox , Matthew Kwan , Lisa Sauermann
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