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A class of non-Gaussian second order random fields

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Author

Summary, in English

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 Mat,rn covariances.

Publishing year

2011

Language

English

Pages

187-222

Publication/Series

Extremes

Volume

14

Issue

2

Document type

Journal article

Publisher

Springer

Topic

  • Probability Theory and Statistics

Keywords

  • Laplace distribution
  • Spectral density
  • Covariance function
  • Stationary
  • second order processes
  • Rice formula

Status

Published

ISBN/ISSN/Other

  • ISSN: 1572-915X