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Anisotropic scaling of the random grain model with application to network traffic
Published online by Cambridge University Press: 24 October 2016
Abstract
We obtain a complete description of anisotropic scaling limits of the random grain model on the plane with heavy-tailed grain area distribution. The scaling limits have either independent or completely dependent increments along one or both coordinate axes and include stable, Gaussian, and ‘intermediate’ infinitely divisible random fields. The asymptotic form of the covariance function of the random grain model is obtained. Application to superimposed network traffic is included.
Keywords
MSC classification
Primary:
60G60: Random fields
- Type
- Research Papers
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- Copyright
- Copyright © Applied Probability Trust 2016
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