Analyzing measurements from data with underlying dependences and heavy-tailed distributions

We consider measurements that are arising from a next generation network and present advanced mathematical techniques to cope with the analysis and modeling of the gathered data. These statistical techniques are required to study important performance indices of new real-time services in a multimedia Internet such as the demanded bandwidth or delay-loss profiles of packet flows during a session. The latter data sets incorporate strongly correlated or long-range dependent time series and heavy-tailed marginal distributions determining the underlying random variables of the data features. To illustrate the proposed statistical analysis concept, we use traces arising from the popular peer-to-peer video streaming application SopCast.

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