Contaminated Variance-Mean mixing model

Fung, T; Wang, JJJ; Seneta, E

Contaminated Variance-Mean mixing model

Fung, T Wang, JJJ

Seneta, E

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Publication Type:: Journal Article
Citation:: Computational Statistics and Data Analysis, 2013, 67 pp. 258 - 267
Issue Date:: 2013-07-03

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Field	Value	Language
dc.contributor.author	Fung, T	en_US
dc.contributor.author	Wang, JJJ https://orcid.org/0000-0003-0088-7003	en_US
dc.contributor.author	Seneta, E	en_US
dc.date.issued	2013-07-03	en_US
dc.identifier.citation	Computational Statistics and Data Analysis, 2013, 67 pp. 258 - 267	en_US
dc.identifier.issn	0167-9473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117174
dc.description.abstract	The Generalised Normal Variance-Mean (GNVM) model in which the mixing random variable is Gamma distributed is considered. This model generalises the popular Variance-Gamma (VG) distribution. This GNVM model can be interpreted as the addition of noise to a (skew) VG base. The discussion is based on goodness of fit criteria and on parameter estimation. The conclusion is that the shape of the VG distribution can be adjusted in a favourable way by adding noise. © 2013 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Computational Statistics and Data Analysis	en_US
dc.relation.isbasedon	10.1016/j.csda.2013.05.024	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Contaminated Variance-Mean mixing model	en_US
dc.type	Journal Article
utslib.citation.volume	67	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	67	en_US

Abstract:

The Generalised Normal Variance-Mean (GNVM) model in which the mixing random variable is Gamma distributed is considered. This model generalises the popular Variance-Gamma (VG) distribution. This GNVM model can be interpreted as the addition of noise to a (skew) VG base. The discussion is based on goodness of fit criteria and on parameter estimation. The conclusion is that the shape of the VG distribution can be adjusted in a favourable way by adding noise. © 2013 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/117174