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The Ornstein-Uhlenbeck process can be seen as a paradigm of a finite-variance and statistically stationary rough random walk. Furthermore, it is defined as the unique solution of a Markovian stochastic dynamics and shares the same local…

Probability · Mathematics 2021-10-05 Laurent Chevillard , Marc Lagoin , Stephane G. Roux

The phenomenon of intermittency has been widely discussed in physics literature. This paper provides a model of intermittency based on L\'evy driven Ornstein-Uhlenbeck (OU) type processes. Discrete superpositions of these processes can be…

Probability · Mathematics 2016-10-12 Danijel Grahovac , Nikolai N. Leonenko , Alla Sikorskii , Irena Tešnjak

We construct a Hunt process that can be described as an isotropic $\alpha$-stable L\'evy process reflected from the complement of a bounded open Lipschitz set. In fact, we introduce a new analytic method for concatenating Markov processes.…

Probability · Mathematics 2024-10-07 Krzysztof Bogdan , Markus Kunze

We study high-dimensional Ornstein--Uhlenbeck processes driven by L\'evy noise and consider drift matrices that decompose into a low-rank plus sparse component, capturing a few latent factors together with a sparse network of direct…

Probability · Mathematics 2026-03-25 Marina Palaisti

Computing the stochastic entropy production associated with the evolution of a stochastic dynamical system is a well-established problem. In a small number of cases such as the Ornstein-Uhlenbeck process, of which we give a complete…

Statistical Mechanics · Physics 2020-08-26 Richard J Martin , Ian J Ford

This paper proposes a simple mathematical model of non-stationary and non-linear stochastic dynamics, which approximates a (globally) non-stationary and non-linear stochastic process by its locally (or \emph{"piecewise"}) stationary…

Superpositions of Ornstein-Uhlenbeck type (supOU) processes provide a rich class of stationary stochastic processes for which the marginal distribution and the dependence structure may be modeled independently. We show that they can also…

Probability · Mathematics 2019-06-14 Danijel Grahovac , Nikolai N. Leonenko , Murad S. Taqqu

Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popular; examples include supervised learning under…

Machine Learning · Computer Science 2024-11-11 Alexandre Galashov , Michalis K. Titsias , András György , Clare Lyle , Razvan Pascanu , Yee Whye Teh , Maneesh Sahani

We consider the problem of efficient estimation of the drift parameter of an Ornstein-Uhlenbeck type process driven by a L\'{e}vy process when high-frequency observations are given. The estimator is constructed from the time-continuous…

Statistics Theory · Mathematics 2014-03-13 Hilmar Mai

We are interested in the law of the first passage time of an Ornstein-Uhlenbeck process to time-varying thresholds. We show that this problem is connected to the laws of the first passage time of the process to members of a two-parameter…

Probability · Mathematics 2024-03-26 Aria Ahari , Larbi Alili , Massimiliano Tamborrino

We solve a physically significant extension of a classic problem in the theory of diffusion, namely the Ornstein-Uhlenbeck process [G. E. Ornstein and L. S. Uhlenbeck, Phys. Rev. 36, 823, (1930)]. Our generalised Ornstein-Uhlenbeck systems…

Statistical Mechanics · Physics 2009-11-11 V. Bezuglyy , B. Mehlig , M. Wilkinson , K. Nakamura , E. Arvedson

In this article we introduce a theory of integration for deterministic, operator-valued integrands with respect to cylindrical L\'evy processes in separable Banach spaces. Here, a cylindrical L\'evy process is understood in the classical…

Probability · Mathematics 2014-05-29 Markus Riedle

We consider the parametric estimation of the Ornstein-Uhlenbeck process driven by a non-Gaussian $\alpha$-stable L\'{e}vy process with the stable index $\alpha>1$ and possibly skewed jumps, based on a discrete-time sample over a fixed…

Statistics Theory · Mathematics 2026-01-28 Eitaro Kawamo , Hiroki Masuda

Generative modeling via stochastic processes has led to remarkable empirical results as well as to recent advances in their theoretical understanding. In principle, both space and time of the processes can be discrete or continuous. In this…

Machine Learning · Statistics 2024-05-07 Ludwig Winkler , Lorenz Richter , Manfred Opper

We study the distribution and various properties of exponential functionals of hypergeometric Levy processes. We derive an explicit formula for the Mellin transform of the exponential functional and give both convergent and asymptotic…

Probability · Mathematics 2010-12-06 Alexey Kuznetsov , Juan Carlos Pardo

In this paper, we establish the existence of transition density for geometric $\alpha$-stable processes by using the property of self-decomposability--a fundamental concept in the theory of L\'evy processes. In contrast to traditional and…

Probability · Mathematics 2026-03-13 Kaneharu Tsuchida

In this article, we introduce Mittag-Leffler L\'evy process and provide two alternative representations of this process. First, in terms of Laplace transform of the marginal densities and next as a subordinated stochastic process. Both…

Probability · Mathematics 2016-02-05 Arun Kumar , N. S. Upadhye

This paper shows that the configurational temperature of liquid-state theory, $\Tc$, defines an energy scale, which can be used for adjusting model parameters of active Ornstein-Uhlenbeck particle (AOUP) models in order to achieve…

Soft Condensed Matter · Physics 2023-02-20 Shibu Saw , Lorenzo Costigliola , Jeppe C. Dyre

In the context of non-equilibrium statistical physics, the entropy production rate is an important concept to describe how far a specific state of a system is from its equilibrium state. In this paper, we establish a central limit theorem…

Probability · Mathematics 2015-09-02 Ran Wang , Lihu Xu

We investigate the problem of estimating the drift parameter of a high-dimensional L\'evy-driven Ornstein--Uhlenbeck process under sparsity constraints. It is shown that both Lasso and Slope estimators achieve the minimax optimal rate of…

Statistics Theory · Mathematics 2022-05-17 Niklas Dexheimer , Claudia Strauch
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