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Multivariate generalized Laplace distribution and related random fields

In English

Författare

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.

Publiceringsår

2013

Språk

Engelska

Sidor

59-72

Publikation/Tidskrift/Serie

Journal of Multivariate Analysis

Volym

113

Dokumenttyp

Artikel i tidskrift

Förlag

Academic Press

Ämne

  • Probability Theory and Statistics

Nyckelord

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

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 0047-259X