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We provide a simple, general argument to obtain improvements of concentration-type inequalities starting from improvements of their corresponding isoperimetric-type inequalities. We apply this argument to obtain robust improvements of the…

Optimization and Control · Mathematics 2015-07-14 Eric A. Carlen , Francesco Maggi

This paper gives a review of concentration inequalities which are widely employed in non-asymptotical analyses of mathematical statistics in a wide range of settings, from distribution-free to distribution-dependent, from sub-Gaussian to…

Statistics Theory · Mathematics 2025-02-24 Huiming Zhang , Song Xi Chen

Concentration inequalities for the sample mean, like those due to Bernstein, Hoeffding, and Bentkus, are valid for any sample size but overly conservative, yielding confidence intervals that are unnecessarily wide. The central limit theorem…

Probability · Mathematics 2025-12-23 Morgane Austern , Lester Mackey

We are concerned with obtaining novel concentration inequalities for the missing mass, i.e. the total probability mass of the outcomes not observed in the sample. We not only derive - for the first time - distribution-free Bernstein-like…

Machine Learning · Statistics 2015-06-22 Bahman Yari Saeed Khanloo , Gholamreza Haffari

We observe that the technique of Markov contraction can be used to establish measure concentration for a broad class of non-contracting chains. In particular, geometric ergodicity provides a simple and versatile framework. This leads to a…

Probability · Mathematics 2013-12-06 Aryeh Kontorovich , Roi Weiss

The aim of this paper is to establish the uniform convergence of the densities of a sequence of random variables, which are functionals of an underlying Gaussian process, to a normal density. Precise estimates for the uniform distance are…

Probability · Mathematics 2013-08-30 Yaozhong Hu , Fei Lu , David Nualart

Let $\pa{X_{t}}_{t\in T}$ be a family of real-valued centered random variables indexed by a countable set $T$. In the first part of this paper, we establish exponential bounds for the deviation probabilities of the supremum $Z=\sup_{t\in…

Statistics Theory · Mathematics 2009-09-11 Yannick Baraud

We study the distribution of Durbin-Wu-Hausman (DWH) and Revankar-Hartley (RH) tests for exogeneity from a finite-sample viewpoint, under the null and alternative hypotheses. We consider linear structural models with possibly non-Gaussian…

Methodology · Statistics 2017-01-26 Firmin Doko Tchatoka , Jean-Marie Dufour

We obtain non-uniform Edgeworth expansions for several classes of weakly dependent (non-stationary) sequences of random variables, including uniformly elliptic inhomogeneous Markov chains, random and time-varying (partially) hyperbolic or…

Probability · Mathematics 2025-11-11 Yeor Hafouta

The main result of the article is the rate of convergence to the Rosenblatt-type distributions in non-central limit theorems. Specifications of the main theorem are discussed for several scenarios. In particular, special attention is paid…

Probability · Mathematics 2016-06-16 Vo Anh , Nikolai Leonenko , Andriy Olenko

We consider the problem of recovering a real-valued $n$-dimensional signal from $m$ phaseless, linear measurements and analyze the amplitude-based non-smooth least squares objective. We establish local convergence of subgradient descent…

Machine Learning · Computer Science 2021-08-31 Paul Hand , Oscar Leong , Vladislav Voroninski

Using the method of transportation-information inequality introduced in \cite{GLWY}, we establish Bernstein type's concentration inequalities for empirical means $\frac 1t \int_0^t g(X_s)ds$ where $g$ is a unbounded observable of the…

Probability · Mathematics 2010-02-11 Fuqing Gao , Arnaud Guillin , Liming Wu

We derive explicit Bernstein-type and Bennett-type concentration inequalities for matrix-valued martingale processes with unbounded observations from the Hermitian space $\mathbb{H}(d)$. Specifically, we assume that the…

Probability · Mathematics 2025-02-21 Alexey Kroshnin , Alexandra Suvorikova

We establish a universal framework for concentration inequalities based on invariance under diffeomorphism groups. Given a probability measure $\mu$ on a space $E$ and a diffeomorphism $\psi: E \to F$, concentration properties transfer…

Statistics Theory · Mathematics 2025-12-12 Jocelyn Nembé

We prove higher order concentration bounds for functions on Stiefel and Grassmann manifolds equipped with the uniform distribution. This partially extends previous work for functions on the unit sphere. Technically, our results are based on…

Probability · Mathematics 2022-08-17 Friedrich Götze , Holger Sambale

A central tool in the study of nonhomogeneous random matrices, the noncommutative Khintchine inequality, yields a nonasymptotic bound on the spectral norm of general Gaussian random matrices $X=\sum_i g_i A_i$ where $g_i$ are independent…

Probability · Mathematics 2023-09-18 Afonso S. Bandeira , March T. Boedihardjo , Ramon van Handel

A sharp, distribution free, non-asymptotic result is proved for the concentration of a random function around the mean function, when the randomization is generated by a finite sequence of independent data and the random functions satisfy…

Probability · Mathematics 2023-12-25 Thomas Anton , Sutanuka Roy , Rabee Tourky

Stein's method for concentration inequalities was introduced to prove concentration of measure in problems involving complex dependencies such as random permutations and Gibbs measures. In this paper, we provide some extensions of the…

Probability · Mathematics 2010-11-11 Sourav Chatterjee , Partha S. Dey

In this paper, we prove the restricted isometry property of block diagonal random matrices with elements from $\varphi$-sub-Gaussian variables, which extends the previously known results for the sub-Gaussian case. A crucial ingredient of…

Probability · Mathematics 2024-11-14 Yiming Chen , Guozheng Dai , Kaiti Ding

We obtain a Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of the underlying Banach norm. We use this inequality in order…

Machine Learning · Statistics 2018-12-11 Gilles Blanchard , Oleksandr Zadorozhnyi