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According to a version of Donsker's theorem, geodesic random walks on Riemannian manifolds converge to the respective Brownian motion. From a computational perspective, however, evaluating geodesics can be quite costly. We therefore…

Probability · Mathematics 2023-12-05 Simon Schwarz , Michael Herrmann , Anja Sturm , Max Wardetzky

Donsker Theorem is perhaps the most famous invariance principle result for Markov processes. It states that when properly normalized, a random walk behaves asymptotically like a Brownian motion. This approach can be extended to general…

Probability · Mathematics 2020-05-29 Eustache Besançon , E Besanç On , Laurent Decreusefond , Pascal Moyal

We consider random walks perturbed at zero which behave like (possibly different) random walks with i.i.d. increments on each half lines and restarts at $0$ whenever they cross that point. We show that the perturbed random walk, after being…

Probability · Mathematics 2019-06-04 Hoang-Long Ngo , Marc Peigne

The original Donsker theorem says that a standard random walk converges in distribution to a Brownian motion in the space of continuous functions. It has recently been extended to enriched random walks and enriched Brownian motion. We use…

Probability · Mathematics 2018-06-18 Laure Coutin , Laurent Decreusefond

In this paper we study approximations for boundary crossing probabilities for the moving sums of i.i.d. normal random variables. We propose approximating a discrete time problem with a continuous time problem allowing us to apply developed…

Statistics Theory · Mathematics 2019-04-30 Jack Noonan , Anatoly Zhigljavsky

We derive explicit formulas for probabilities of Brownian motion with jumps crossing linear or piecewise linear boundaries in any finite interval. We then use these formulas to approximate the boundary crossing probabilities for general…

Probability · Mathematics 2012-05-16 Jinghai Shao , Liqun Wang

We use the language of errors to handle local Dirichlet forms with square field operator (cf [2]). Let us consider, under the hypotheses of Donsker theorem, a random walk converging weakly to a Brownian motion. If in addition the random…

Probability · Mathematics 2007-05-23 Nicolas Bouleau

A theorem of Donsker asserts that the empirical process converges in distribution to the Brownian bridge. The aim of this paper is to provide a new and simple proof of this fact.

Probability · Mathematics 2008-03-21 Jean-François Marckert

We construct the conditional version of $k$ independent and identically distributed random walks on $\R$ given that they stay in strict order at all times. This is a generalisation of so-called non-colliding or non-intersecting random…

Probability · Mathematics 2007-05-23 Peter Eichelsbacher , Wolfgang Konig

We consider random walks with independent but not necessarily identical distributed increments. Assuming that the increments satisfy the well-known Lindeberg condition, we investigate the asymptotic behaviour of first-passage times over…

Probability · Mathematics 2016-11-03 Denis Denisov , Alexander Sakhanenko , Vitali Wachtel

In a recent paper of Eichelsbacher and Koenig (2008) the model of ordered random walks has been considered. There it has been shown that, under certain moment conditions, one can construct a k-dimensional random walk conditioned to stay in…

Probability · Mathematics 2009-07-17 D. Denisov , V. Wachtel

The purpose of this note is to collect in one place a few results about simple random walk and Brownian motion which are often useful. These include standard results such as Beurling estimates, large deviation estimates, and a method for…

Probability · Mathematics 2007-05-23 Christian Benes

The main purpose of this work is to define planar self-intersection local time by an alternative approach which is based on an almost sure pathwise approximation of planar Brownian motion by simple, symmetric random walks. As a result,…

Probability · Mathematics 2012-11-27 Tamás Szabados

Donsker's invariance principle is shown to hold for random walks in rough path topology. As application, we obtain Donsker-type weak limit theorems for stochastic integrals and differential equations.

Probability · Mathematics 2008-10-16 Emmanuel Breuillard , Peter Friz , Martin Huesmann

In this note - starting from $d$-dimensional (with $d>1$) fuzzy vectors - we prove Donsker's classical invariance principle. We consider a fuzzy random walk ${S^*_n}=X^*_1+\cdots+X^*_n,$ where $\{X^*_i\}_1^{\infty}$ is a sequence of…

Probability · Mathematics 2017-09-04 Jan Schneider , Roman Urban

A simple random walk and a Brownian motion are considered on a spider that is a collection of half lines (we call them legs) joined in the origin. We give a strong approximation of these two objects and their local times. For fixed number…

Probability · Mathematics 2017-05-12 Endre Csaki , Miklos Csorgo , Antonia Foldes , Pal Revesz

Several classical results on boundary crossing probabilities of Brownian motion and random walks are extended to asymptotically Gaussian random fields, which include sums of i.i.d. random variables with multidimensional indices,…

Probability · Mathematics 2007-05-23 Hock Peng Chan , Tze Leung Lai

We establish an invariance principle connecting boundary random walks on $\mathbb N$ with Feller's Brownian motions on $[0,\infty)$. A Feller's Brownian motion is a Feller process on $[0,\infty)$ whose excursions away from the boundary $0$…

Probability · Mathematics 2026-01-22 Liping Li , Zhangjie Wang

The usual random walk on a group (homogeneous both in time and in space) is determined by a probability measure on the group. In a random walk with random transition probabilities this single measure is replaced with a stationary sequence…

Probability · Mathematics 2007-05-23 Vadim A. Kaimanovich , Yuri Kifer , Ben-Zion Rubshtein

In this paper we study approximations for the boundary crossing probabilities of moving sums of i.i.d. normal r.v. We approximate a discrete time problem with a continuous time problem allowing us to apply established theory for stationary…

Statistics Theory · Mathematics 2020-01-06 Jack Noonan , Anatoly Zhigljavsky
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