Estimating the Heavy Tail Index from Scaling Properties

Mark E. Crovella () and Murad S. Taqqu ()
Additional contact information
Mark E. Crovella: Boston University
Murad S. Taqqu: Boston University

Methodology and Computing in Applied Probability, 1999, vol. 1, issue 1, 55-79

Abstract: Abstract This paper deals with the estimation of the tail index α for empirical heavy-tailed distributions, such as have been encountered in telecommunication systems. We present a method (called the “scaling estimator”) based on the scaling properties of sums of heavy-tailed random variables. It has the advantages of being nonparametric, of being easy to apply, of yielding a single value, and of being relatively accurate on synthetic datasets. Since the method relies on the scaling of sums, it measures a property that is often one of the most important effects of heavy-tailed behavior. Most importantly, we present evidence that the scaling estimator appears to increase in accuracy as the size of the dataset grows. It is thus particularly suited for large datasets, as are increasingly encountered in measurements of telecommunications and computing systems.

Keywords: estimation; file sizes; heavy tails; World Wide Web (search for similar items in EconPapers)
Date: 1999
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1023/A:1010012224103 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:metcap:v:1:y:1999:i:1:d:10.1023_a:1010012224103

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/11009

DOI: 10.1023/A:1010012224103

Access Statistics for this article

Methodology and Computing in Applied Probability is currently edited by Joseph Glaz

More articles in Methodology and Computing in Applied Probability from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().