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We study central limit theorems for certain nonlinear sequences of random variables. In particular, we prove the central limit theorems for the bounded conductivity of the random resistor networks on hierarchical lattices.

Disordered Systems and Neural Networks · Physics 2007-05-23 Jung M. Woo , Jan Wehr

Approximations of fractional Brownian motion using Poisson processes whose parameter sets have the same dimensions as the approximated processes have been studied in the literature. In this paper, a special approximation to the…

Statistics Theory · Mathematics 2012-01-05 Yuqiang Li , Hongshuai Dai

We study the asymptotic behaviour of additive functionals of random walks in random scenery. We establish bounds for the moments of the local time of the Kesten and Spitzer process.These bounds combined with a previous moment convergence…

Dynamical Systems · Mathematics 2021-01-05 Françoise Pene

We study functional limit theorems for linear type processes with short memory under the assumption that the innovations are dependent identically distributed random variables with infinite variance and in the domain of attraction of stable…

Probability · Mathematics 2010-05-20 Marta Tyran-Kaminska

We prove a functional central limit theorem for subgraph counts in a dynamic version of the random connection model. To establish tightness, we develop a dynamic extension of the cumulant method.

Probability · Mathematics 2025-11-25 Rajat Subhra Hazra , Nikolai Kriukov , Michel Mandjes , Moritz Otto

This article investigates general scaling settings and limit distributions of functionals of filtered random fields. The filters are defined by the convolution of non-random kernels with functions of Gaussian random fields. The case of…

Probability · Mathematics 2018-12-19 Tareq Alodat , Nikolai Leonenko , Andriy Olenko

We use a Stochastic Differential Equation satisfied by Brownian motion taking values in the unit sphere $S_{n-1}subsetmathbb{R}^{n}$ and we obtain a Central Limit Theorem for a sequence of such Brownian motions. We also generalize the…

Probability · Mathematics 2011-11-30 Stavros Vakeroudis , Marc Yor

Anomalous diffusion is an established phenomenon but still a theoretical challenge in non-equilibrium statistical mechanics. Physical models are built incrementally, and the most recent and most general family is based on the fractional…

Probability · Mathematics 2025-07-23 Christian Bender , Yana A. Butko , Mirko D'Ovidio , Gianni Pagnini

We study the central limit theorem in the non-normal domain of attraction to symmetric $\alpha$-stable laws for $0<\alpha\leq2$. We show that for i.i.d. random variables $X_i$, the convergence rate in $L^\infty$ of both the densities and…

Probability · Mathematics 2018-04-24 Christoph Börgers , Claude Greengard

We consider empirical processes associated with high-frequency observations of a fractional Brownian motion (fBm) $X$ with Hurst parameter $H\in (0,1)$, and derive conditions under which these processes verify a (possibly uniform) law of…

Probability · Mathematics 2019-04-09 Arturo Jaramillo , Ivan Nourdin , Giovanni Peccati

In this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. Then, applying this lemma to weakly dependent processes…

Statistics Theory · Mathematics 2007-06-13 Jean-Marc Bardet , Paul Doukhan , Gabriel Lang , Nicolas Ragache

In this article we consider L\'evy driven continuous time moving average processes observed on a lattice, which are stationary time series. We show asymptotic normality of the sample mean, the sample autocovariances and the sample…

Probability · Mathematics 2012-06-15 Serge Cohen , Alexander Lindner

In this paper, we present the asymptotic theory for integrated functions of increments of Brownian local times in space. Specifically, we determine their first-order limit, along with the asymptotic distribution of the fluctuations. Our key…

Probability · Mathematics 2023-11-03 Simon Campese , Nicolas Lengert , Mark Podolskij

Under an appropriate regular variation condition, the affinely normalized partial sums of a sequence of independent and identically distributed random variables converges weakly to a non-Gaussian stable random variable. A functional version…

Probability · Mathematics 2012-10-12 Bojan Basrak , Danijel Krizmanić , Johan Segers

We give general conditions for the central limit theorem and weak convergence to Brownian motion (the weak invariance principle / functional central limit theorem) to hold for observables of compact group extensions of nonuniformly…

Dynamical Systems · Mathematics 2016-08-25 Georg A. Gottwald , Ian Melbourne

In [Kozma-Toth, Ann. Probab. v 45, pp 4307-4347 (2017)] the weak CLT was established for random walks in doubly stochastic (or, divergence-free) random environments, under the following conditions: 1. Strict ellipticity assumed for the…

Probability · Mathematics 2025-01-03 Bálint Tóth

We prove the annealed Central Limit Theorem for random walks in bistochastic random environments on $Z^d$ with zero local drift. The proof is based on a "dynamicist's interpretation" of the system, and requires a much weaker condition than…

Probability · Mathematics 2009-06-22 Marco Lenci

We combine the method of exchangeable pairs with Stein's method for functional approximation. As a result, we give a general linearity condition under which an abstract Gaussian approximation theorem for stochastic processes holds. We apply…

Probability · Mathematics 2020-10-22 Mikolaj J. Kasprzak

Suppose that $\{X_t,\,t\ge0\}$ is a non-stationary Markov process, taking values in a Polish metric space $E$. We prove the law of large numbers and central limit theorem for an additive functional of the form $\int_0^T\psi(X_s)ds$,…

Probability · Mathematics 2012-03-26 Tomasz Komorowski , Anna Walczuk

We study the asymptotic shape of the trajectory of the stochastic gradient descent algorithm applied to a convex objective function. Under mild regularity assumptions, we prove a functional central limit theorem for the properly rescaled…

Machine Learning · Statistics 2026-02-18 Kessang Flamand , Victor-Emmanuel Brunel