CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

Multivariate generalized Laplace distribution and related random fields

T. J. Kozubowski ; K. Podgorski ; Igor Rychlik (Institutionen för matematiska vetenskaper, matematisk statistik)
Journal of Multivariate Analysis (0047-259X). Vol. 113 (2013), Special Issue: SI, p. 59-72.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Bessel function distribution, Laplace distribution, Moving average processes, Stochastic field, roughness, coherence



Denna post skapades 2012-12-20.
CPL Pubid: 168461

 

Läs direkt!


Länk till annan sajt (kan kräva inloggning)


Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)

Ämnesområden

Matematisk statistik

Chalmers infrastruktur