The normal inverse Gaussian distribution as a model for MUI

The normal inverse Gaussian (NIG) distribution has previously been shown to be a versatile tool to model heavy-tailed processes. A cumulant-matching estimator of the NIG parameters was introduced in Oigard and Hansen (2001). Here, we analyze the performance of this estimator. Next, we study whether...

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Main Authors: Salberg, A.-B., Swami, A., Oigard, T.A., Hanssen, A.
Format: Conference Proceeding
Language:English
Subjects:
Gaussian distribution
Hydrogen
Interference
Parameter estimation
Performance analysis
Physics
Random variables
Shape
Tail
Testing
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creator Salberg, A.-B.
Swami, A.
Oigard, T.A.
Hanssen, A.
description The normal inverse Gaussian (NIG) distribution has previously been shown to be a versatile tool to model heavy-tailed processes. A cumulant-matching estimator of the NIG parameters was introduced in Oigard and Hansen (2001). Here, we analyze the performance of this estimator. Next, we study whether NIG models are appropriate models for multi-user interference. Finally, we empirically test the goodness of fit of squared-NIG models to tele-traffic data, which have been fitted with alpha-stable models.
doi_str_mv 10.1109/ACSSC.2001.987735
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source IEEE Electronic Library (IEL) Conference Proceedings
subjects Gaussian distribution
Hydrogen
Interference
Parameter estimation
Performance analysis
Physics
Random variables
Shape
Tail
Testing
title The normal inverse Gaussian distribution as a model for MUI
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