English

Stationary systems of Gaussian processes

Probability 2010-11-16 v2

Abstract

We describe all countable particle systems on R\mathbb{R} which have the following three properties: independence, Gaussianity and stationarity. More precisely, we consider particles on the real line starting at the points of a Poisson point process with intensity measure m\mathfrak{m} and moving independently of each other according to the law of some Gaussian process ξ\xi. We classify all pairs (m,ξ)(\mathfrak{m},\xi) generating a stationary particle system, obtaining three families of examples. In the first, trivial family, the measure m\mathfrak{m} is arbitrary, whereas the process ξ\xi is stationary. In the second family, the measure m\mathfrak{m} is a multiple of the Lebesgue measure, and ξ\xi is essentially a Gaussian stationary increment process with linear drift. In the third, most interesting family, the measure m\mathfrak{m} has a density of the form αeλx\alpha e^{-\lambda x}, where α>0\alpha >0, λR\lambda\in\mathbb{R}, whereas the process ξ\xi is of the form ξ(t)=W(t)λσ2(t)/2+c\xi(t)=W(t)-\lambda\sigma ^2(t)/2+c, where WW is a zero-mean Gaussian process with stationary increments, σ2(t)=VarW(t)\sigma ^2(t)=\operatorname {Var}W(t), and cRc\in\mathbb{R}.

Keywords

Cite

@article{arxiv.0903.2738,
  title  = {Stationary systems of Gaussian processes},
  author = {Zakhar Kabluchko},
  journal= {arXiv preprint arXiv:0903.2738},
  year   = {2010}
}

Comments

Published in at http://dx.doi.org/10.1214/10-AAP686 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)

R2 v1 2026-06-21T12:41:01.912Z