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This paper is devoted to the study of large deviation behaviors in the setting of the estimation of the regression function on functional data. A large deviation principle is stated for a process Zn, defined below, allowing to derive a…

Statistics Theory · Mathematics 2016-11-25 Djamal Louani , Sidi Mohamed Ould Maouloud

We derive probabilistic limit theorems that reveal the intricate structure of the phase transitions in a mean-field version of the Blume-Emery-Griffiths model. These probabilistic limit theorems consist of scaling limits for the total spin…

Statistical Mechanics · Physics 2015-06-25 Marius Costeniuc , Richard S. Ellis , Peter Tak-Hun Otto

Let $L = (L(t))_{t\geq 0}$ be a multivariate L\'evy process with L\'evy measure $\nu(dy) = \exp(-f(|y|)) dy$ for a smoothly regularly varying function $f$ of index $\alpha>1$. The process $L$ is renormalized as $X^\varepsilon(t) =…

Probability · Mathematics 2025-06-02 Michael A. Högele , Torsten Wetzel

Laplace-type results characterize the limit of sequence of measures $(\pi_\varepsilon)_{\varepsilon >0}$ with density w.r.t the Lebesgue measure $(\mathrm{d} \pi_\varepsilon / \mathrm{d} \mathrm{Leb})(x) \propto \exp[-U(x)/\varepsilon]$…

Probability · Mathematics 2026-04-29 Valentin De Bortoli , Agnès Desolneux

The study of two-dimensional Coulomb gases lies at the interface of statistical physics and non-Hermitian random matrix theory. In this paper we give a large deviation principle (LDP) for the empirical fields obtained, under the canonical…

Probability · Mathematics 2015-10-07 Thomas Leblé

Based on stochastic curvilinear integrals in the Cairoli-Walsh sense and in the It\^{o}-Udri\c{s}te sense, we develop an original theory regarding the multitime stochastic differential systems. The first group of the original results refer…

Optimization and Control · Mathematics 2011-12-06 Constantin Udriste , Virgil Damian

We develop a space-time large-deviation point of view on Gibbs-non-Gibbs transitions in spin systems subject to a stochastic spin-flip dynamics. Using the general theory for large deviations of functionals of Markov processes outlined in…

Probability · Mathematics 2015-03-17 Aernout van Enter , Roberto Fernández , Frank den Hollander , Frank Redig

We study a class of dissipative PDE's perturbed by an unbounded kick force. Under some natural assumptions, the restrictions of solutions to integer times form a homogeneous Markov process. Assuming that the noise is rough with respect to…

Mathematical Physics · Physics 2014-10-20 Vojkan Jaksic , Vahagn Nersesyan , Claude-Alain Pillet , Armen Shirikyan

Large deviation theory (LDT) provides a mathematical framework to quantify the probabilities of rare events in stochastic systems. In this study, we applied LDT to model a chemical reaction system and demonstrated that the fluctuation…

Chemical Physics · Physics 2025-01-22 Tatsuaki Tsuruyama

In this paper, we study the large deviation principle (LDP) for two types (Type I and Type II) of multiplicative Ising models. For Types I and II, the explicit formulas for the free energy functions and the associated rate functions are…

Dynamical Systems · Mathematics 2023-05-16 Jung-Chao Ban , Wen-Guei Hu , Zongfan Zhang

In this paper we consider the multispecies stirring process on the discrete torus. We prove a large deviation principle for the trajectory of the vector of densities of the different species. The technique of proof consists in extending the…

Probability · Mathematics 2024-10-29 Francesco Casini , Frank Redig , Hidde van Wiechen

In this paper we study the large deviation behavior of sums of i.i.d. random variables X_i defined on a supercritical Galton-Watson process Z. We assume the finiteness of the moments EX_1^2 and EZ_1log Z_1. The underlying interplay of the…

Probability · Mathematics 2007-06-13 Klaus Fleischmann , Vitali Wachtel

We establish large deviation principles for the largest eigenvalue of large random matrices with variance profiles. For $N \in \mathbb N$, we consider random $N \times N$ symmetric matrices $H^N$ which are such that…

Probability · Mathematics 2024-03-25 Raphaël Ducatez , Alice Guionnet , Jonathan Husson

In many stochastic models, the observables of interest are naturally encoded in double transforms (e.g., Laplace transforms) that couple spatial and temporal variables. Notably, the double transform often provides the only analytically…

Probability · Mathematics 2026-05-21 Giampaolo Cristadoro , Gaia Pozzoli

We establish, under the Cramer exponential moment condition in a neighbourhood of zero, the Extended Large Deviation Principle for the Random Walk and the Compound Poisson processes in the metric space $\V$ of functions of finite variation…

Probability · Mathematics 2016-11-01 F. C. Klebaner , A. A. Mogulskii

To investigate the complex dynamics of a biological neuron that is subject to small random perturbations we can use stochastic neuron models. While many techniques have already been developed to study properties of such models, especially…

Neurons and Cognition · Quantitative Biology 2017-07-18 Jan H. Kirchner

The theory of quantum jump trajectories provides a new framework for understanding dynamical phase transitions in open systems. A candidate for such transitions is the atom maser, which for certain parameters exhibits strong intermittency…

Quantum Physics · Physics 2024-06-19 Federico Girotti , Merlijn van Horssen , Raffaella Carbone , Madalin Guta

In a recent paper, Aistleitner, Gantert, Kabluchko, Prochno and Ramanan studied large deviation principles (LDPs) for lacunary trigonometric sums $\sum_{n=1}^N \cos(2 \pi n_k x)$, where the sequence $(n_k)_{k \geq 1}$ satisfies the Hadamard…

Probability · Mathematics 2025-07-30 Christoph Aistleitner , Lorenz Frühwirth , Manuel Hauke , Maryna Manskova

In contrast to the study of Langevin equations in a homogeneous environment in the literature, the study on Langevin equations in randomly-varying environments is relatively scarce. Almost all the existing works require random environments…

Probability · Mathematics 2021-08-25 Nhu N. Nguyen , George Yin

For the Ornstein-Uhlenbeck process, the asymptotic behavior of the maximum likelihood estimator of the drift parameter is totally different in the stable, unstable, and explosive cases. Notwithstanding of this trichotomy, we investigate…

Probability · Mathematics 2011-11-28 Bernard Bercu , Laure Coutin , Nicolas Savy
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