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This paper proves joint convergence of the approximation error for several stochastic integrals with respect to local Brownian semimartingales, for nonequidistant and random grids. The conditions needed for convergence are that the Lebesgue…

Probability · Mathematics 2013-09-24 Carl Lindberg , Holger Rootzén

In this note, we consider Szemer\'{e}di's theorem on $k$-term arithmetic progressions over finite fields $\mathbb{F}_p^n$, where the allowed set $S$ of common differences in these progressions is chosen randomly of fixed size. Combining a…

Number Theory · Mathematics 2025-08-05 Jason Zheng

This paper considers discrete-time linear systems with bounded additive disturbances, and studies the convergence properties of the backward reachable sets of robust controlled invariant sets (RCIS). Under a simple condition, we prove that…

Systems and Control · Electrical Eng. & Systems 2023-09-28 Zexiang Liu , Necmiye Ozay

We study distribution dependent stochastic differential equations with irregular, possibly distributional drift, driven by an additive fractional Brownian motion of Hurst parameter $H\in (0,1)$. We establish strong well-posedness under a…

Probability · Mathematics 2021-06-01 Lucio Galeati , Fabian A. Harang , Avi Mayorcas

Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. In…

Machine Learning · Computer Science 2014-04-17 Navodit Misra , Ercan E. Kuruoglu

This paper derives two stabilizability theorems for a basic class of discrete-time nonlinear systems with multiple unknown parameters. First, we claim that a discrete-time multi-parameter system is stabilizable if its nonlinear growth rate…

Optimization and Control · Mathematics 2020-07-23 Zhaobo Liu , Chanying Li

We prove a dynamical restriction principle, asserting that every restriction estimate satisfied by the Fourier transform in $\mathbb{R}^d$ is also valid for the propagator of certain Schr\"odinger equations. We consider smooth Hamiltonians…

Analysis of PDEs · Mathematics 2026-03-26 Fabio Nicola

This paper presents some asymptotic results for statistics of Brownian semi-stationary (BSS) processes. More precisely, we consider power variations of BSS processes, which are based on high frequency (possibly higher order) differences of…

Probability · Mathematics 2013-06-04 José Manuel Corcuera , Emil Hedevang , Mikko S. Pakkanen , Mark Podolskij

Here we establish the central limit theorem for a class of stochastic partial differential equations (SPDEs) and as an application derive this theorem for two widely studied population models known as super-Brownian motion and Fleming-Viot…

Probability · Mathematics 2014-04-22 Parisa Fatheddin

We consider a stochastic model for the dynamics of the two-sided limit order book (LOB). Our model is flexible enough to allow for a dependence of the price dynamics on volumes. For the joint dynamics of best bid and ask prices and the…

Mathematical Finance · Quantitative Finance 2016-08-04 Christian Bayer , Ulrich Horst , Jinniao Qiu

This paper is motivated by the problem of quantitatively bounding the convergence of adaptive control methods for stochastic systems to a stationary distribution. Such bounds are useful for analyzing statistics of trajectories and…

Optimization and Control · Mathematics 2021-10-19 Tyler Lekang , Andrew Lamperski

The existence of random dynamical systems for McKean--Vlasov SDEs is established. This is approached by considering the joint dynamics of the corresponding nonlinear Fokker-Planck equation governing the law of the system and the underlying…

Probability · Mathematics 2025-07-04 Benjamin Gess , Rishabh S. Gvalani , Shanshan Hu

In this paper, we are concerned with the symmetric simple exclusion process (SSEP) on the regular tree $\mathcal{T}_d$. A central limit theorem and a moderate deviation principle of the additive functional of the process are proved, which…

Probability · Mathematics 2025-04-23 Xiaofeng Xue

This paper develops limit theorems for random variables with network dependence, without requiring the individuals in the network to be located in a Euclidean or metric space. This distinguishes our approach from most existing limit…

Econometrics · Economics 2026-03-20 Wen Jiang , Yachen Wang , Zeqi Wu , Xingbai Xu

We prove that the statistical properties of random perturbations of a nonuniformly hyperbolic diffeomorphism are described by a finite number of stationary measures. We also give necessary and sufficient conditions for the stochastic…

Dynamical Systems · Mathematics 2011-11-10 Jose F. Alves , Vitor Araujo , Carlos H. Vasquez

We study the saddlepoint approximation (SPA) for sums of $n$ i.i.d. random vectors $X_i\in\mathbb R^d$ in growing dimensions. SPA provides highly accurate approximations to probability densities and distribution functions via the moment…

Probability · Mathematics 2025-10-27 Alexander Katsevich

Berry-Esseen bounds for non-linear functionals of infinite Rademacher sequences are derived by means of the Malliavin-Stein method. Moreover, multivariate extensions for vectors of Rademacher functionals are shown. The results establish a…

Probability · Mathematics 2017-11-06 Kai Krokowski , Anselm Reichenbachs , Christoph Thaele

Existence, uniqueness, and $L_p$-approximation results are presented for scalar stochastic differential equations (SDEs) by considering the case where, the drift coefficient has finitely many spatial discontinuities while both coefficients…

Probability · Mathematics 2022-04-06 Thomas Müller-Gronbach , Sotirios Sabanis , Larisa Yaroslavtseva

By tightening the conventional Lieb-Robinson bounds to better handle systems which lack translation invariance, we determine the extent to which "weak links" suppress operator growth in disordered one-dimensional spin chains. In particular,…

Disordered Systems and Neural Networks · Physics 2024-04-23 Christopher L. Baldwin , Adam Ehrenberg , Andrew Y. Guo , Alexey V. Gorshkov

We extend the Gibbs conditioning principle to an abstract setting combining infinitely many linear equality constraints and non-linear inequality constraints, which need not be convex. A conditional large large deviation principle (LDP) is…

Functional Analysis · Mathematics 2024-10-29 Louis-Pierre Chaintron , Giovanni Conforti , Julien Reygner
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