A class of non-Gaussian second order random fields
Journal article, 2011

Non-Gaussian stochastic fields are introduced by means of integrals with respect to independently scattered stochastic measures distributed according to generalized Laplace laws. In particular, we discuss stationary second order random fields that, as opposed to their Gaussian counterpart, have a possibility of accounting for asymmetry and heavier tails. Additionally to this greater flexibility the models discussed continue to share most spectral properties with Gaussian processes. Their statistical distributions at crossing levels are computed numerically via the generalized Rice formula. The potential for stochastic modeling of real life phenomena that deviate from the Gaussian paradigm is exemplified by a stochastic field model with Matérn covariances.

infinitely divisible processes

Stationary

Covariance function

Laplace distribution

Rice formula

Spectral density

second order processes

Author

Sofia Åberg

Chalmers, Mathematical Sciences, Mathematical Statistics

University of Gothenburg

K. Podgorski

Lund University

Extremes

1386-1999 (ISSN) 1572915x (eISSN)

Vol. 14 2 187-222

Subject Categories

Probability Theory and Statistics

DOI

10.1007/s10687-010-0119-1

More information

Latest update

3/2/2018 9