Estimation for Stochastic Models Driven by Laplace Motion
(2011) In Communications in Statistics: Theory and Methods 40(18). p.3281-3302- Abstract
- Laplace motion is a Levy process built upon Laplace distributions. Non Gaussian stochastic fields that are integrals with respect to this process are considered and methods for their model fitting are discussed. The proposed procedures allow for inference about the parameters of the underlying Laplace distributions. A fit of dependence structure is also addressed. The importance of a convenient parameterization that admits natural and consistent estimation for this class of models is emphasized. Several parameterizations are introduced and their advantages over one another discussed. The proposed estimation method targets the standard characteristics: mean, variance, skewness and kurtosis. Their sample equivalents are matched in the... (More)
- Laplace motion is a Levy process built upon Laplace distributions. Non Gaussian stochastic fields that are integrals with respect to this process are considered and methods for their model fitting are discussed. The proposed procedures allow for inference about the parameters of the underlying Laplace distributions. A fit of dependence structure is also addressed. The importance of a convenient parameterization that admits natural and consistent estimation for this class of models is emphasized. Several parameterizations are introduced and their advantages over one another discussed. The proposed estimation method targets the standard characteristics: mean, variance, skewness and kurtosis. Their sample equivalents are matched in the closest possible way as allowed by natural constraints within this class. A simulation study and an example of potential applications conclude the article. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/2187153
- author
- Podgorski, Krzysztof LU and Wegener, Jörg LU
- organization
- publishing date
- 2011
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Kurtosis, Laplace distribution, Method of moment estimation, Moving, averages, Skewness, Stochastic fields
- in
- Communications in Statistics: Theory and Methods
- volume
- 40
- issue
- 18
- pages
- 3281 - 3302
- publisher
- Marcel Dekker
- external identifiers
-
- wos:000294890500007
- scopus:79960451666
- ISSN
- 0361-0926
- DOI
- 10.1080/03610926.2010.499051
- language
- English
- LU publication?
- yes
- id
- 3efdb095-51d2-4d66-92d1-7c1807e8c719 (old id 2187153)
- date added to LUP
- 2016-04-01 13:34:34
- date last changed
- 2022-01-27 19:53:06
@article{3efdb095-51d2-4d66-92d1-7c1807e8c719,
abstract = {{Laplace motion is a Levy process built upon Laplace distributions. Non Gaussian stochastic fields that are integrals with respect to this process are considered and methods for their model fitting are discussed. The proposed procedures allow for inference about the parameters of the underlying Laplace distributions. A fit of dependence structure is also addressed. The importance of a convenient parameterization that admits natural and consistent estimation for this class of models is emphasized. Several parameterizations are introduced and their advantages over one another discussed. The proposed estimation method targets the standard characteristics: mean, variance, skewness and kurtosis. Their sample equivalents are matched in the closest possible way as allowed by natural constraints within this class. A simulation study and an example of potential applications conclude the article.}},
author = {{Podgorski, Krzysztof and Wegener, Jörg}},
issn = {{0361-0926}},
keywords = {{Kurtosis; Laplace distribution; Method of moment estimation; Moving; averages; Skewness; Stochastic fields}},
language = {{eng}},
number = {{18}},
pages = {{3281--3302}},
publisher = {{Marcel Dekker}},
series = {{Communications in Statistics: Theory and Methods}},
title = {{Estimation for Stochastic Models Driven by Laplace Motion}},
url = {{http://dx.doi.org/10.1080/03610926.2010.499051}},
doi = {{10.1080/03610926.2010.499051}},
volume = {{40}},
year = {{2011}},
}