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Multivariate generalized Laplace distribution and related random fields

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Author

Summary, in English

Multivariate Laplace distribution is an important stochastic model that accounts for asymmetry and heavier than Gaussian tails, while still ensuring the existence of the second moments. A Levy process based on this multivariate infinitely divisible distribution is known as Laplace motion, and its marginal distributions are multivariate generalized Laplace laws. We review their basic properties and discuss a construction of a class of moving average vector processes driven by multivariate Laplace motion. These stochastic models extend to vector fields, which are multivariate both in the argument and the value. They provide an attractive alternative to those based on Gaussianity, in presence of asymmetry and heavy tails in empirical data. An example from engineering shows modeling potential of this construction. (C) 2012 Elsevier Inc. All rights reserved.

Publishing year

2013

Language

English

Pages

59-72

Publication/Series

Journal of Multivariate Analysis

Volume

113

Document type

Journal article

Publisher

Academic Press

Topic

  • Probability Theory and Statistics

Keywords

  • Bessel function distribution
  • Laplace distribution
  • Moving average
  • processes
  • Stochastic field

Status

Published

ISBN/ISSN/Other

  • ISSN: 0047-259X