Multivariate generalized Laplace distribution and related random fields
(2013) In Journal of Multivariate Analysis 113. p.59-72- Abstract
- 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... (More)
- 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/3401197
- author
- Kozubowski, Tomasz J. ; Podgorski, Krzysztof LU and Rychlik, Igor
- organization
- publishing date
- 2013
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Bessel function distribution, Laplace distribution, Moving average, processes, Stochastic field
- in
- Journal of Multivariate Analysis
- volume
- 113
- pages
- 59 - 72
- publisher
- Academic Press
- external identifiers
-
- wos:000310865300007
- scopus:84867703621
- ISSN
- 0047-259X
- DOI
- 10.1016/j.jmva.2012.02.010
- language
- English
- LU publication?
- yes
- id
- 70baef52-24d4-4539-861e-e031de59e1f5 (old id 3401197)
- date added to LUP
- 2016-04-01 13:10:37
- date last changed
- 2022-01-27 17:45:30
@article{70baef52-24d4-4539-861e-e031de59e1f5,
abstract = {{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.}},
author = {{Kozubowski, Tomasz J. and Podgorski, Krzysztof and Rychlik, Igor}},
issn = {{0047-259X}},
keywords = {{Bessel function distribution; Laplace distribution; Moving average; processes; Stochastic field}},
language = {{eng}},
pages = {{59--72}},
publisher = {{Academic Press}},
series = {{Journal of Multivariate Analysis}},
title = {{Multivariate generalized Laplace distribution and related random fields}},
url = {{http://dx.doi.org/10.1016/j.jmva.2012.02.010}},
doi = {{10.1016/j.jmva.2012.02.010}},
volume = {{113}},
year = {{2013}},
}