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Let $(X_i)_{i\geq 1}$ be a stationary mean-zero Gaussian process with covariances $\rho(k)=\PE(X_{1}X_{k+1})$ satisfying: $\rho(0)=1$ and $\rho(k)=k^{-D} L(k)$ where $D$ is in $(0,1)$ and $L$ is slowly varying at infinity. Consider the…

Statistics Theory · Mathematics 2010-12-08 Céline Lévy-Leduc , Hélène Boistard , Eric Moulines , Murad S. Taqqu , Valderio A. Reisen

The stability of an Auto-Regressive (AR) time sequence of finite order $L$, is determined by the maximal modulus $r^\star$ among all zeros of its generating polynomial. If $r^\star<1$ then the effect of input and initial conditions decays…

Probability · Mathematics 2019-06-04 Amir Dembo , Jian Ding , Jun Yan

For the moving average process $X_n=\rho \xi_{n-1}+\xi_n$, $n\in\mathbb{N}$, where $\rho\in\mathbb{R}$ and $(\xi_i)_{i\ge -1}$ is an i.i.d. sequence of normally distributed random variables, we study the persistence probabilities…

Probability · Mathematics 2024-07-10 Frank Aurzada , Dieter Bothe , Pierre-Étienne Druet , Marvin Kettner , Christophe Profeta

A moderate deviation principle for nonlinear functions of Gaussian processes is established. The nonlinear functions need not be locally bounded. Especially, the logarithm is allowed. (Thus, small deviations of the process are relevant.)…

Probability · Mathematics 2007-05-23 Boris Tsirelson

We study the a.s. sample path regularity of Gaussian processes. To this end we relate the path regularity directly to the theory of small deviations. In particular, we show that if the process is $n$-times differentiable then the…

Probability · Mathematics 2009-05-21 Frank Aurzada

We revisit a result of Mittal--Ylvisaker that states that the rescaled maximum of a stationary sequence of Gaussian random variables has a Gaussian limit if correlations decay sufficiently slowly. Taking a new approach we relax the…

Probability · Mathematics 2026-05-21 Jason Li , Stephen Muirhead

Persistence, defined as the probability that a fluctuating signal has not reached a threshold up to a given observation time, plays a crucial role in the theory of random processes. It quantifies the kinetics of processes as varied as phase…

Statistical Mechanics · Physics 2022-10-12 N. Levernier , T. V. Mendes , O. Bénichou , R. Voituriez , T. Guérin

We consider the class of Markovian processes defined by the equation $\dd x /\dd t = -\beta x + \sum_k z_k \delta (t-t_k)$. Such processes are encountered in systems (like coalescing systems) where dynamics creates discrete upward jumps at…

Statistical Mechanics · Physics 2009-10-31 Olivier Deloubriere

We establish upper bounds on the rate of decay of correlations of tower systems with summable variation of the Jacobian and integrable return time. That is, we consider situations in which the Jacobian is not Holder and the return time is…

Dynamical Systems · Mathematics 2007-05-23 Jerome Buzzi , Veronique Maume-Deschamps

The probabilities $P_\pm(t_0,t)$ that a growing Kardar-Parisi-Zhang interface remains above or below the mean height in the time interval $(t_0, t)$ are shown numerically to decay as $P_\pm \sim (t_0/t)^{\theta_\pm}$ with $\theta_+ = 1.18…

Statistical Mechanics · Physics 2009-10-31 Harald Kallabis , Joachim Krug

We establish a sprinkled decoupling inequality for increasing events of Gaussian vectors with an error that depends only on the maximum pairwise correlation. As an application we prove the non-triviality of the percolation phase transition…

Probability · Mathematics 2023-07-18 Stephen Muirhead

The fraction r(t) of spins which have never flipped up to time t is studied within a linear diffusion approximation to phase ordering. Numerical simulations show that, even in this simple context, r(t) decays with time like a power-law with…

Condensed Matter · Physics 2009-10-28 Bernard Derrida , Vincent Hakim , Reuven Zeitak

We study the long-time behavior of the probability density Q_t of the first exit time from a bounded interval [-L,L] for a stochastic non-Markovian process h(t) describing fluctuations at a given point of a two-dimensional, infinite in both…

Statistical Mechanics · Physics 2008-01-28 G. Oshanin

Let $X(t)=(X_1(t), \dots, X_n(t)), t\in \mathcal{T}\subset \mathbb{R} $ be a centered vector-valued Gaussian process with independent components and continuous trajectories, and $h(t)=(h_1(t),\dots, h_n(t)), t\in \mathcal{T} $ be a…

Probability · Mathematics 2018-01-09 Long Bai , Krzysztof Debicki , Peng Liu

We consider a periodic Ising chain with nearest-neighbour and $r$-th neighbour interaction and quench it from infinite temperature to zero temperature. The persistence probability $P(t)$, measured as the probability that a spin remains…

Statistical Mechanics · Physics 2008-03-14 Anjan Kumar Chandra , Subinay Dasgupta

Classic results by L.-S. Young show that the decay of correlations for systems that admit inducing schemes can be obtained through the recurrence rates of the inducing scheme. Reciprocal results were obtained for non-invertible systems…

Dynamical Systems · Mathematics 2025-10-29 José F. Alves , João S. Matias

Let $\{X_i(t),t\ge0\}, 1\le i\le n$ be independent centered stationary Gaussian processes with unit variance and almost surely continuous sample paths. For given positive constants $u,T$, define the set of conjunctions $C_{[0,T],u}:=\{t\in…

Probability · Mathematics 2014-10-08 Krzysztof Dȩbicki , Enkelejd Hashorva , Lanpeng Ji , Kamil Tabis

We consider an one-dimensional lattice system of unbounded and continuous spins. The Hamiltonian consists of a perturbed strictly-convex single-site potential and with longe-range interaction. We show that if the interactions decay…

Probability · Mathematics 2015-06-17 Georg Menz , Robin Nittka

In this article, we consider additive functionals $\zeta_t = \int_0^t f(X_s)\mathrm{d} s$ of a c\`adl\`ag Markov process $(X_t)_{t\geq 0}$ on $\mathbb{R}$. Under some general conditions on the process $(X_t)_{t\geq 0}$ and on the function…

Probability · Mathematics 2023-04-19 Quentin Berger , Loïc Béthencourt , Camille Tardif

Let $\{X_i(t),t\ge0\}, 1\le i\le n$ be independent copies of a stationary process $\{X(t), t\ge0\}$. For given positive constants $u,T$, define the set of $r$th conjunctions $ C_{r,T,u}:= \{t\in [0,T]: X_{r:n}(t) > u\}$ with $X_{r:n}(t)$…

Probability · Mathematics 2014-08-07 Krzysztof Debicki , Enkelejd Hashorva , Lanpeng Ji , Chengxiu Ling