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Under Poincar\'e-type conditions, upper bounds are explored for the Kolmogorov distance between the distributions of weighted sums of dependent summands and the normal law. Based on improved concentration inequalities on high-dimensional…

Probability · Mathematics 2020-11-19 S. G. Bobkov , G. P. Chistyakov , F. Götze

We propose a general approach for quantitative convergence analysis of non-reversible Markov processes, based on the concept of second-order lifts and a variational approach to hypocoercivity. To this end, we introduce the flow Poincar{\'e}…

Analysis of PDEs · Mathematics 2025-07-22 Andreas Eberle , Arnaud Guillin , Leo Hahn , Francis Lörler , Manon Michel

We develop a unified strategy to obtain the geometric logarithmic Hardy inequality on any open set M of a stratified group, provided the validity of the Hardy inequality in this setting, where the so-called "weight" is regarded to be any…

Functional Analysis · Mathematics 2024-02-19 Marianna Chatzakou

We prove new concentration estimates for random variables that are functionals of a Poisson measure defined on a general measure space. Our results are specifically adapted to geometric applications, and are based on a pervasive use of a…

Probability · Mathematics 2015-04-14 Sascha Bachmann , Giovanni Peccati

We investigate boundary estimates for elliptic operators with stationary random coefficients exhibiting integrable correlations, arising from stochastic homogenization theory. As practical applications, we establish decay estimates for…

Analysis of PDEs · Mathematics 2026-02-12 Li Wang , Qiang Xu

We consider Gaussian measures $\mu, \tilde{\mu}$ on a separable Hilbert space, with fractional-order covariance operators $A^{-2\beta}$ resp. $\tilde{A}^{-2\tilde{\beta}}$, and derive necessary and sufficient conditions on $A, \tilde{A}$…

Probability · Mathematics 2023-07-19 David Bolin , Kristin Kirchner

In the paper [25], written in collaboration with Gesine Reinert, we proved a universality principle for the Gaussian Wiener chaos. In the present work, we aim at providing an original example of application of this principle in the…

Probability · Mathematics 2010-02-08 Ivan Nourdin , Giovanni Peccati

In this article, we introduce and develop the notion of parametrised Poincar\'{e} duality in the formalism of parametrised higher category theory by Martini-Wolf, in part generalising Cnossen's theory of twisted ambidexterity to the…

Algebraic Topology · Mathematics 2024-05-31 Kaif Hilman , Dominik Kirstein , Christian Kremer

This work is an application of the second order gauge theory for the Lorentz group, where a description of the gravitational interaction is obtained which includes derivatives of the curvature. We analyze the form of the second field…

General Relativity and Quantum Cosmology · Physics 2015-02-24 R. R. Cuzinatto , C. A. M. de Melo , L. G. Medeiros , P. J. Pompeia

In this paper we obtain non-uniform Berry-Esseen bounds for normal approximations by the Malliavin-Stein method. The techniques rely on a detailed analysis of the solutions of Stein's equations and will be applied to functionals of a…

Probability · Mathematics 2024-09-17 Marius Butzek , Peter Eichelsbacher

Using a modified version of Schauder's fixed point theorem, measures of non-compactness and classical techniques, we provide new general results on the asymptotic behavior and the non-oscillation of second order scalar nonlinear…

Classical Analysis and ODEs · Mathematics 2007-05-23 Angelo B. Mingarelli , Kishin Sadarangani

In this paper we obtain the non - asymptotic estimations of Poincare type between function and its gradient in the so - called Bilateral Grand Lebesgue Spaces. We also give some examples to show the sharpness of these inequalities.

Functional Analysis · Mathematics 2009-08-06 E. Ostrovsky , L. Sirota , E. Rogover

A class of second order approximations, called the weighted and shifted Gr\"{u}nwald difference operators, are proposed for Riemann-Liouville fractional derivatives, with their effective applications to numerically solving space fractional…

Numerical Analysis · Mathematics 2015-04-27 WenYi Tian , Han Zhou , Weihua Deng

In this paper, we consider the explicit bound for the second-order approximation of the quadratic variation of a general fractional Gaussian process $(G_t)_{t\ge 0}$. The second order mixed partial derivative of the covariance function $…

Probability · Mathematics 2021-06-18 Yong Chen , Zhen Ding , Ying Li

We introduce a framework to derive quantitative central limit theorems in the context of non-linear approximation of Gaussian random variables taking values in a separable Hilbert space. In particular, our method provides an alternative to…

Probability · Mathematics 2020-11-25 Solesne Bourguin , Simon Campese

We propose algebraic criteria that yield sharp H\"{o}lder types of inequalities for the product of functions of Gaussian random vectors with arbitrary covariance structure. While our lower inequality appears to be new, we prove that the…

Probability · Mathematics 2013-10-08 Wei-Kuo Chen , Nikos Dafnis , Grigoris Paouris

The paper deals about Hardy-type inequalities associated with the following higher order Poincar\'e inequality: $$ \left( \frac{N-1}{2} \right)^{2(k -l)} := \inf_{ u \in C_{c}^{\infty} \setminus \{0\}} \frac{\int_{\mathbb{H}^{N}}…

Classical Analysis and ODEs · Mathematics 2015-11-03 Elvise Berchio , Debdip Ganguly

In the context of mod-Gaussian convergence, as defined previously in our work with J. Jacod, we obtain lower bounds for local probabilities for a sequence of random vectors which are approximately Gaussian with increasing covariance. This…

Number Theory · Mathematics 2014-02-26 E. Kowalski , A. Nikeghbali

Most correlation inequalities for high-dimensional functions in the literature, such as the Fortuin-Kasteleyn-Ginibre (FKG) inequality and the celebrated Gaussian Correlation Inequality of Royen, are qualitative statements which establish…

Probability · Mathematics 2020-12-23 Anindya De , Shivam Nadimpalli , Rocco A. Servedio

Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017)…

Machine Learning · Statistics 2021-11-04 Andrés F. López-Lopera , François Bachoc , Nicolas Durrande , Olivier Roustant
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