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We study the counting of level crossings for inertial random processes exposed to stochastic resetting events. We develop the general approach of stochastic resetting for inertial processes with sudden changes in the state characterized by…

Statistical Mechanics · Physics 2023-12-22 Miquel Montero , Matteo Palassini , Jaume Masoliver

We discuss a family of random fields indexed by a parameter $s\in \mathbb{R}$ which we call the fractional Gaussian fields, given by \[ \mathrm{FGF}_s(\mathbb{R}^d)=(-\Delta)^{-s/2} W, \] where $W$ is a white noise on $\mathbb{R}^d$ and…

Probability · Mathematics 2016-02-08 Asad Lodhia , Scott Sheffield , Xin Sun , Samuel S. Watson

We examine the mean first passage time for a particle driven by highly correlated Gaussian fluctuations to reach one or more predetermined boundaries. We discuss a numerical algorithm to generate power-law correlated fluctuations and apply…

Statistical Mechanics · Physics 2007-05-23 Aldo H. Romero , J. M. Sancho , Katja Lindenberg

Stretched Gaussian distribution is the fundamental solution of the Hausdorff derivative diffusion equation and its corresponding stretched Gaussian noise is a widely encountered non-Gaussian noise in science and engineering. The least…

Numerical Analysis · Mathematics 2019-06-17 Wei Xu , Yingjie Liang , Wen Chen

In high-energy physics it is a recurring challenge to efficiently and precisely (enough) calculate the global significance of, e.g., a potential new resonance. We propose a new method that models the significance in the search region as a…

Methodology · Statistics 2023-06-19 V. Ananiev , A. L. Read

We develop a GMM approach for estimation of log-normal stochastic volatility models driven by a fractional Brownian motion with unrestricted Hurst exponent. We show that a parameter estimator based on the integrated variance is consistent…

Statistical Finance · Quantitative Finance 2026-01-16 Anine E. Bolko , Kim Christensen , Mikko S. Pakkanen , Bezirgen Veliyev

This paper discusses the fractional diffusion equation forced by a tempered fractional Gaussian noise. The fractional diffusion equation governs the probability density function of the subordinated killed Brownian motion. The tempered…

Numerical Analysis · Mathematics 2020-07-14 Xing Liu , Weihua Deng

The problem of (pathwise) large deviations for conditionally continuous Gaussian processes is investigated. The theory of large deviations for Gaussian processes is extended to the wider class of random processes -- the conditionally…

Probability · Mathematics 2019-02-07 Barbara Pacchiarotti , Alessandro Pigliacelli

We determine the exact Hausdorff measure functions for the range and level sets of a class of Gaussian random fields satisfying sectorial local nondeterminism and other assumptions. We also establish a Chung-type law of the iterated…

Probability · Mathematics 2020-12-08 Cheuk Yin Lee

In this article, we consider the problem of estimating fractional processes based on noisy high-frequency data. Generalizing the idea of pre-averaging to a fractional setting, we exhibit a sequence of consistent estimators for the unknown…

Statistics Theory · Mathematics 2026-01-14 David Chen , Yu Cheng , Carsten Chong , Pierre Gentine , Wangdong Jia , Bryce Monier , Shiyang Shen

The paper deals with the asymptotic behavior of the bridge of a Gaussian process conditioned to stay in $n$ fixed points at $n$ fixed past instants. In particular, functional large deviation results are stated for small time. Several…

Probability · Mathematics 2016-04-06 L. Caramellino , B. Pacchiarotti

Equalization-enhanced Phase Noise causes burst-like distortions in high symbol-rate transmission systems. We propose a temporal Gaussian noise model that captures these distortions by introducing a time-varying distortion power. Validated…

Signal Processing · Electrical Eng. & Systems 2025-07-14 Benedikt Geiger , Fred Buchali , Vahid Aref , Laurent Schmalen

Closed-form expressions, parametrized by the Hurst exponent $H$ and the length $n$ of a time series, are derived for paths of fractional Brownian motion (fBm) and fractional Gaussian noise (fGn) in the $\mathcal{A}-\mathcal{T}$ plane,…

Data Analysis, Statistics and Probability · Physics 2020-01-01 Mariusz Tarnopolski

The long range dependence of the fractional Brownian motion (fBm), fractional Gaussian noise (fGn), and differentiated fGn (DfGn) is described by the Hurst exponent $H$. Considering the realisations of these three processes as time series,…

Data Analysis, Statistics and Probability · Physics 2016-07-20 Mariusz Tarnopolski

Additive or multiplicative stationary noise recently became an important issue in applied fields such as microscopy or satellite imaging. Relatively few works address the design of dedicated denoising methods compared to the usual white…

Computer Vision and Pattern Recognition · Computer Science 2013-07-18 Jérôme Fehrenbach , Pierre Weiss

The intensity statistics of signals in the presence of Gaussian noise is obtained by studying the model of a random signal plus a random phasor sum. The additive Gaussian noise is shown to result in a Bessel transform of the probability…

Disordered Systems and Neural Networks · Physics 2015-05-13 A. A. Chabanov

In a common experimental setting, the behaviour of a noisy dynamical system is monitored in response to manipulations of one or more control parameters. Here, we introduce a structured model to describe parametric changes in qualitative…

Dynamical Systems · Mathematics 2018-07-05 Gergo Bohner , Maneesh Sahani

We study simple approximations to fractional Gaussian noise and fractional Brownian motion. The approximations are based on spectral properties of the noise. They allow one to consider the noise as the result of fractional…

Statistical Mechanics · Physics 2007-05-23 A. V. Chechkin , V. Yu. Gonchar

Simple analytically solvable models are proposed exhibiting 1/f spectrum in wide range of frequency. The signals of the models consist of pulses (point process) which interevent times fluctuate about some average value, obeying an…

Statistical Mechanics · Physics 2007-05-23 B. Kaulakys , T. Meskauskas

We present a stochastic version of the mean-shift clustering algorithm. In this stochastic version a randomly chosen sequence of data points move according to partial gradient ascent steps of the objective function. Theoretical results…

Machine Learning · Computer Science 2025-11-13 Itshak Lapidot , Yann Sepulcre , Tom Trigano
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