Contaminated Variance-Mean mixing model

Thomas Fung; Joanna J J Wang; Eugene Seneta

doi:10.1016/j.csda.2013.05.024

Contaminated Variance-Mean mixing model

Thomas Fung^*, Joanna J J Wang, Eugene Seneta

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	258-267
Number of pages	10
Journal	Computational Statistics and Data Analysis
Volume	67
DOIs	https://doi.org/10.1016/j.csda.2013.05.024
Publication status	Published - 2013

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10.1016/j.csda.2013.05.024

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title = "Contaminated Variance-Mean mixing model",

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.",

author = "Thomas Fung and Wang, {Joanna J J} and Eugene Seneta",

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doi = "10.1016/j.csda.2013.05.024",

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AU - Wang, Joanna J J

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AB - 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.

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SN - 0167-9473

VL - 67

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EP - 267

JO - Computational Statistics and Data Analysis

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Contaminated Variance-Mean mixing model

Abstract

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