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We study the upper tail behaviors of the local times of the additive L\'{e}vy processes and additive random walks. The limit forms we establish are the moderate deviations and the laws of the iterated logarithm for the L_2-norms of the…

Probability · Mathematics 2011-11-10 Xia Chen

For sequences of non-lattice weakly dependent random variables, we obtain asymptotic expansions for Large Deviation Principles. These expansions, commonly referred to as strong large deviation results, are in the spirit of Edgeworth…

Probability · Mathematics 2020-03-10 Kasun Fernando , Pratima Hebbar

As an important tool characterizing the long time behavior of Markov processes, the Donsker-Varadhan LDP (large deviation principle) does not directly apply to distribution dependent SDEs/SPDEs since the solutions are non-Markovian. We…

Probability · Mathematics 2020-02-21 Panpan Ren , Feng-Yu Wang

In this paper, we investigate the asymptotic behavior of supercritical branching Markov processes $\{\mathbb{X}_t, t \ge0\}$ whose spatial motions are L\'evy processes with regularly varying tails. Recently, Ren et al. [Appl. Probab. 61…

Probability · Mathematics 2025-10-01 Runjia Luo , Yan-Xia Ren , Renming Song , Rui Zhang

Using the Wiener-Hopf factorization, it is shown that it is possible to bound the path of an arbitrary Levy process above and below by the paths of two random walks. These walks have the same step distribution, but different random starting…

Probability · Mathematics 2007-05-23 R. A. Doney

We establish a comprehensive sample path large deviation principle (LDP) for log-processes associated with multivariate time-inhomogeneous stochastic volatility models. Examples of models for which the new LDP holds include Gaussian models,…

Probability · Mathematics 2022-11-15 Archil Gulisashvili

We obtain asymptotic expansions for the large deviation principle (LDP) for continuous time stochastic processes with weakly dependent increments. As a key example, we show that additive functionals of solutions of stochastic differential…

Probability · Mathematics 2021-04-06 Kasun Fernando , Pratima Hebbar

This paper is devoted to the problem of sample path large deviations for the Markov processes on R_+^N having a constant but different transition mechanism on each boundary set {x:x_i=0 for i\notin\Lambda, x_i>0 for i\in\Lambda}. The global…

Probability · Mathematics 2007-05-23 Irina Ignatiouk-Robert

In biological, glassy, and active systems, various tracers exhibit Laplace-like, i.e., exponential, spreading of the diffusing packet of particles. The limitations of the central limit theorem in fully capturing the behaviors of such…

Statistical Mechanics · Physics 2024-02-22 Omer Hamdi , Stanislav Burov , Eli Barkai

Large and moderate deviation probabilities play an important role in many applied areas, such as insurance and risk analysis. This paper studies the exact moderate and large deviation asymptotics in non-logarithmic form for linear processes…

Statistics Theory · Mathematics 2013-05-07 Magda Peligrad , Hailin Sang , Yunda Zhong , Wei Biao Wu

Rare events in stochastic processes with heavy-tailed distributions are controlled by the big jump principle, which states that a rare large fluctuation is produced by a single event and not by an accumulation of coherent small deviations.…

Statistical Mechanics · Physics 2020-03-13 Raffaella Burioni , Alessandro Vezzani

For certain natural families of topologies, we study continuity and stability of statistical properties of random walks on linear groups over local fields. We extend large deviation results known in the Archimedean case to non-Archimedean…

Probability · Mathematics 2025-05-21 Omar Hurtado , Sidhanth Raman

Starting with the large deviation principle (LDP) for the Erd\H{o}s-R\'enyi binomial random graph $\mathcal{G}(n,p)$ (edge indicators are i.i.d.), due to Chatterjee and Varadhan (2011), we derive the LDP for the uniform random graph…

Probability · Mathematics 2018-05-01 Amir Dembo , Eyal Lubetzky

Veraverbeke's (1977) theorem relates the tail of the distribution of the supremum of a random walk with negative drift to the tail of the distribution of its increments, or equivalently, the probability that a centered random walk with…

Probability · Mathematics 2008-02-26 Philippe Barbe , Bill McCormick

In this work, we study large deviation properties of the covariance process in fully connected Gaussian deep neural networks. More precisely, we establish a large deviation principle (LDP) for the covariance process in a functional…

Probability · Mathematics 2025-05-14 Luisa Andreis , Federico Bassetti , Christian Hirsch

One-dimensional run-and-tumble processes may converge towards some localized non-equilibrium steady state when the two velocities and/or the two switching rates are space-dependent. A long dynamical trajectory can be then analyzed via the…

Statistical Mechanics · Physics 2021-08-23 Cecile Monthus

Extreme events are by nature rare and difficult to predict, yet are often much more important than frequent, typical events. An interesting counterpoint to the prediction of such events is their retrodiction -- given a process in an outlier…

Probability · Mathematics 2022-11-21 Wesley W. Erickson , Daniel A. Steck

We examine a distributional fixed-point equation related to a multi-type branching process that is key in the cluster sizes analysis of multivariate heavy-tailed Hawkes processes. Specifically, we explore the tail behavior of its solution…

Probability · Mathematics 2025-04-07 Jose Blanchet , Roger J. A. Laeven , Xingyu Wang , Bert Zwart

In this paper, we establish the precise asymptotic behaviors of the tail probability and the transition density of a large class of isotropic L\'evy processes when the scaling order is between 0 and 2 including 2. We also obtain the precise…

Probability · Mathematics 2017-08-30 Panki Kim , Ante Mimica

We study point process convergence for sequences of iid random walks. The objective is to derive asymptotic theory for the extremes of these random walks. We show convergence of the maximum random walk to the Gumbel distribution under the…

Probability · Mathematics 2020-11-10 Johannes Heiny , Thomas Mikosch , Jorge Yslas