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This paper presents a general approach to linear stochastic processes driven by various random noises. Mathematically, such processes are described by linear stochastic differential equations of arbitrary order (the simplest non-trivial…

Condensed Matter · Physics 2009-10-28 Alon Drory

We study controlled differential equations driven by a rough path (in the sense of T. Lyons) with an additional, possibly unbounded drift term. We show that the equation induces a solution flow if the drift grows at most linearly.…

Probability · Mathematics 2016-05-19 Sebastian Riedel , Michael Scheutzow

We consider a random process as a solution of stochastic differential equations with dependence of the coefficients on small parameter $\varepsilon$ and we suppose that the drift coefficients of these equations are unbounded on the…

Probability · Mathematics 2023-12-15 Ivan H. Krykun

The normalised partial sums of values of a nonnegative multiplicative function over divisors with appropriately restricted sizes of a random permutation from the symmetric group define trajectories of a stochastic process. We prove a…

Probability · Mathematics 2026-01-14 Eugenijus Manstavičius

We derive an invariance principle for the lift to the rough path topology of stochastic processes with delayed regenerative increments under an optimal moment condition. An interesting feature of the result is the emergence of area anomaly,…

Probability · Mathematics 2021-01-14 Tal Orenshtein

We consider controlled differential equations and give new estimates for higher order Euler schemes. Our proofs are inspired by recent work of A. M. Davie who considers first and second order schemes. In order to implement the general case…

Classical Analysis and ODEs · Mathematics 2007-05-23 Peter Friz , Nicolas Victoir

We consider rough differential equations whose coefficients contain path-dependent bounded variation terms and prove the existence and a priori estimate of solutions. These equations include classical path-dependent SDEs containing running…

Probability · Mathematics 2024-03-12 Shigeki Aida

Rough paths techniques give the ability to define solutions of stochastic differential equations driven by signals $X$ which are not semimartingales and whose $p$-variation is finite only for large values of $p$. In this context, rough…

Probability · Mathematics 2020-05-15 Yanghui Liu , Zachary Selk , Samy Tindel

A theory of differential equations driven by a non-differentiable path has recently been developed by Lyons. We develop an alternative approach to this theory, using (modified Euler approximations), and investigate its applicability to…

Probability · Mathematics 2007-10-04 A. M. Davie

In this article, we consider limit theorems for some weighted type random sums (or discrete rough integrals). We introduce a general transfer principle from limit theorems for unweighted sums to limit theorems for weighted sums via rough…

Probability · Mathematics 2017-07-07 Yanghui Liu , Samy Tindel

In the spirit of Marcus canonical stochastic differential equations, we study a similar notion of rough differential equations (RDEs), notably dropping the assumption of continuity prevalent in the rough path literature. A new metric is…

Probability · Mathematics 2019-02-12 Ilya Chevyrev , Peter K. Friz

The paper is split in two parts: in the first part, we construct the exact likelihood for a discretely observed rough differential equation, driven by a piecewise linear path. In the second part, we use this likelihood in order to construct…

Statistics Theory · Mathematics 2018-07-10 Anastasia Papavasiliou , Kasia B. Taylor

We consider the rough differential equation $dY=f(Y)d\bm \om$ where $\bm \om=(\omega,\bbomega)$ is a rough path defined by a Brownian motion $\omega$ on $\RR^m$. Under the usual regularity assumption on $f$, namely $f\in C^3_b (\RR^d,…

Probability · Mathematics 2020-02-25 Hongjun Gao , María J. Garrido-Atienza , Anhui Gu , Kening Lu , Björn Schmalfuss

In this article we develop a new methodology to prove weak approximation results for general stochastic differential equations. Instead of using a partial differential equation approach as is usually done for diffusions, the approach…

Probability · Mathematics 2016-08-16 Emmanuelle Clément , Arturo Kohatsu-Higa , Damien Lamberton

The theory of rough paths arose from a desire to establish continuity properties of ordinary differential equations involving terms of low regularity. While essentially an analytic theory, its main motivation and applications are in…

Classical Analysis and ODEs · Mathematics 2025-01-28 Ilya Chevyrev

As a general rule, differential equations driven by a multi-dimensional irregular path $\Gamma$ are solved by constructing a rough path over $\Gamma$. The domain of definition ? and also estimates ? of the solutions depend on upper bounds…

Probability · Mathematics 2009-05-07 Jérémie Unterberger

We analyze common lifts of stochastic processes to rough paths/rough drivers-valued processes and give sufficient conditions for the cocycle property to hold for these lifts. We show that random rough differential equations driven by such…

Probability · Mathematics 2016-12-07 Ismael Bailleul , Sebastian Riedel , Michael Scheutzow

One problem of wide interest involves estimating expected crossing-times. Several tools have been developed to solve this problem beginning with the works of Wald and the theory of sequential analysis. An extension of his approach is…

Methodology · Statistics 2015-06-17 Mark Brown , Victor de la Pena , Tony Sit

We study rough differential equations driven by controlled rough paths in the level-$2$ regime $1/3<\alpha\le 1/2$. Given a reference rough path $\mathbf X=(1,X,\mathbb X)$ and an $\mathbf X$-controlled driver $\mathbf Z=(Z,Z')$, we first…

Probability · Mathematics 2026-05-12 Nannan Li , Xing Gao

We introduce deterministic perturbation schemes for the recently proposed random directions stochastic approximation (RDSA) [17], and propose new first-order and second-order algorithms. In the latter case, these are the first second-order…

Optimization and Control · Mathematics 2019-03-29 Prashanth L A , Shalabh Bhatnagar , Nirav Bhavsar , Michael Fu , Steven I. Marcus
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