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The paper studies solutions of stochastic partial differential equations with random initial conditions. First, it overviews some of the known results on scaled solutions of such equations and provides several explicit motivating examples.…

Probability · Mathematics 2025-09-30 Maha Mosaad A Alghamdi , Nikolai Leonenko , Andriy Olenko

Consider the $n \times n$ reverse circulant $RC_n(t)$ and symmetric circulant $SC_n(t)$ matrices with independent Brownian motion entries. We discuss the process convergence of the time dependent fluctuations of linear eigenvalue statistics…

Probability · Mathematics 2024-06-19 Arup Bose , Shambhu Nath Maurya , Koushik Saha

A finite range interacting particle system on a transitive graph is considered. Assuming that the dynamics and the initial measure are invariant, the normalized empirical distribution process converges in distribution to a centered…

Mathematical Physics · Physics 2007-05-23 Paul Doukhan , Gabriel Lang , Sana Louhichi , Bernard Ycart

Some probabilistic aspects of the number variance statistic are investigated. Infinite systems of independent Brownian motions and symmetric alpha-stable processes are used to construct new examples of processes which exhibit both divergent…

Probability · Mathematics 2007-05-23 Ben Hambly , Liza Jones

We consider a system of diffusing particles on the real line in a quadratic external potential and with repulsive electrostatic interaction. The empirical measure process is known to converge weakly to a deterministic measure-valued process…

Probability · Mathematics 2010-03-23 Martin Bender

Suppose $X = (X_x, x$ in $Z^d)$ is a family of i.i.d. variables in some measurable space, $B_0$ is a bounded set in $R^d$, and for $t > 1$, $H_t$ is a measure on $tB_0$ determined by the restriction of $X$ to lattice sites in or adjacent to…

Probability · Mathematics 2007-05-23 Mathew D Penrose

In this paper, we develop necessary and sufficient conditions for the validity of a martingale approximation for the partial sums of a stationary process in terms of the maximum of consecutive errors. Such an approximation is useful for…

Probability · Mathematics 2011-02-11 Mikhail Gordin , Magda Peligrad

In this paper we investigate a sequence of square integrable random processes with space varying memory. We establish sufficient conditions for the central limit theorem in the space $L^2(\mu)$ for the partial sums of the sequence of random…

Probability · Mathematics 2015-09-02 Vaidotas Characiejus , Alfredas Račkauskas

This paper focuses on limit theorems for linear Hawkes processes with random marks. We prove a large deviation principle, which answers the question raised by Bordenave and Torrisi. A central limit theorem is also obtained. We conclude with…

Probability · Mathematics 2015-09-15 Dmytro Karabash , Lingjiong Zhu

In this paper, we studied the functional ergodic limits of the site-dependent branching Brownian motions in R. The results show that the limiting processes are non-degenerate if and only if the variance functions of branching laws are…

Probability · Mathematics 2011-08-19 Yuqiang LI

A regime-switching multivariate time series model which is closed under margins is built. The model imposes a restriction on all lower-dimensional sub-processes to follow a regime-switching process sharing the same latent regime sequence…

Methodology · Statistics 2023-12-19 Lin Zhang , Harry Joe , Natalia Nolde

By means of white noise analysis, we prove some limit theorems for nonlinear functionals of a given Volterra process. In particular, our results apply to fractional Brownian motion (fBm) and should be compared with the classical convergence…

Probability · Mathematics 2010-11-30 Sébastien Darses , Ivan Nourdin , David Nualart

An ordinary differential equation perturbed by a null-recurrent diffusion will be considered in the case where the averaging type perturbation is strong only when a fast motion is close to the origin. The normal deviations of these…

Probability · Mathematics 2015-08-24 Zsolt Pajor-Gyulai , Michael Salins

We consider a modification of the covariance function in Gaussian processes to correctly account for known linear constraints. By modelling the target function as a transformation of an underlying function, the constraints are explicitly…

Machine Learning · Statistics 2017-09-20 Carl Jidling , Niklas Wahlström , Adrian Wills , Thomas B. Schön

A workload model using the infinite source Poisson model for bursts is combined with the on--off model for within burst activity. Burst durations and on--off durations are assumed to have heavy-tailed distributions with infinite variance…

Statistics Theory · Mathematics 2011-06-06 David A. Rolls

A joint limit theorem for the point process of the off-diagonal entries of a sample covariance matrix $\mathbf{S}$, constructed from $n$ observations of a $p$-dimensional random vector with iid components, and the Frobenius norm of…

Probability · Mathematics 2023-02-28 Johannes Heiny , Carolin Kleemann

We prove limit theorems for sums of randomly chosen random variables conditioned on the summands. We consider several versions of the corner growth setting, including specific cases of dependence amongst the summands and summands with heavy…

Probability · Mathematics 2022-07-01 David Grzybowski

In the present work we derive a Central Limit Theorem for sequences of Hilbert-valued Piecewise Deterministic Markov process models and their global fluctuations around their deterministic limit identified by the Law of Large Numbers. We…

Probability · Mathematics 2013-04-23 Martin G Riedler , Michele Thieullen

In this paper, we establish some functional central limit theorems for a large class of general supercritical superprocesses with spatially dependent branching mechanisms satisfying a second moment condition. In the particular case when the…

Probability · Mathematics 2014-10-08 Yan-Xia Ren , Renming Song , Rui Zhang

For the challenging task of modeling multivariate time series, we propose a new class of models that use dependent Mat\'ern processes to capture the underlying structure of data, explain their interdependencies, and predict their unknown…

Machine Learning · Statistics 2015-02-13 Alexander Vandenberg-Rodes , Babak Shahbaba
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