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Bootstrapping Heavy-Tailed Data and Extremes
2003In this chapter, we consider two topics, viz., bootstrapping heavy-tailed time series data and bootstrapping the extremes (i.e., the maxima and the minima) of stationary processes. We call a random variable heavy-tailed if its variance is infinite. For iid random variables with such heavy tails, it is well known (cf.
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Long-range dependence and heavy-tail modeling for teletraffic data
IEEE Signal Processing Magazine, 2002The analysis and modeling of computer network traffic is a daunting task considering the amount of available data. This is quite obvious when considering the spatial dimension of the problem, since the number of interacting computers, gateways and switches can easily reach several thousands, even in a local area network (LAN) setting. This is also true
Cappé, Olivier +4 more
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Fitting and Predicting Heavy Tailed Insurance Data
SSRN Electronic Journal, 2008Predicting the severity of insurance claims is not difficult when there is correspondence between the observed data and a chosen parametric model. However, for heavy tailed insurance data it is hard to find a parametric model that is sophisticated enough to both predict the tail behavior and fit the body of the data. In the litterature it seems to be a
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TESTING FOR LINEAR DEPENDENCE IN HEAVY-TAILED DATA
Communications in Statistics - Theory and Methods, 2002We use the sample covariation to develop tests for lagged linear dependence in symmetric time series data. We propose tests for both finite and infinite variance processes. The finite sample performance of the tests is investigated using simulated data and compared to tests based on the von Neumann ratio.
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A practical method for analysing heavy tailed data
Canadian Journal of Statistics, 2009AbstractAn important practical issue of applying heavy tailed distributions is how to choose the sample fraction or threshold, since only a fraction of upper order statistics can be employed in the inference. Recently, Guillou & Hall (2001; Journal of Royal Statistical Society B, 63, 293–305) proposed a simple way to choose the threshold in ...
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Graph Learning for Balanced Clustering of Heavy-Tailed Data
2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2023Amirhossein Javaheri +2 more
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The Heavy-Tailed Gleser Model: Properties, Estimation, and Applications
Mathematics, 2022Osvaldo Venegas +2 more
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