Boston University Libraries OpenBU
    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    •   OpenBU
    • BU Open Access Articles
    • BU Open Access Articles
    • View Item
    •   OpenBU
    • BU Open Access Articles
    • BU Open Access Articles
    • View Item

    Estimating heavy-tail exponents through max self-similarity

    Thumbnail
    Date Issued
    2011-03-01
    Publisher Version
    10.1109/TIT.2010.2103751
    Author(s)
    Stoev, Stilian A.
    Michailidis, George
    Taqqu, Murad S.
    Share to FacebookShare to TwitterShare by Email
    Export Citation
    Download to BibTex
    Download to EndNote/RefMan (RIS)
    Metadata
    Show full item record
    Permanent Link
    https://hdl.handle.net/2144/37414
    Version
    First author draft
    Citation (published version)
    Stilian A Stoev, George Michailidis, Murad S Taqqu. 2011. "Estimating Heavy-Tail Exponents Through Max Self-Similarity." IEEE TRANSACTIONS ON INFORMATION THEORY, Volume 57, Issue 3, pp. 1615 - 1636 (22). https://doi.org/10.1109/TIT.2010.2103751
    Abstract
    In this paper, a novel approach to the problem of estimating the heavy-tail exponent α >; 0 of a distribution is proposed. It is based on the fact that block-maxima of size m scale at a rate m 1/α for independent, as well as for a number of dependent data. This scaling rate can be captured well by the max-spectrum plot of the data that leads to regression based estimators for α. Consistency and asymptotic normality of these estimators is established for independent data under mild conditions on the behavior of the tail of the distribution. The proposed estimators have an important computational advantage over existing methods; namely, they can be calculated and updated sequentially in an on-line fashion without having to store the entire data set. Practical issues on the automatic selection of tuning parameters for the estimators and corresponding confidence intervals are also addressed. Extensive numerical simulations show that the proposed method is competitive for both small and large sample sizes, robust to contaminants and continues to work under the presence of substantial amount of dependence. The proposed estimators are used to illustrate the close connection between long-range dependence and heavy tails over an Internet traffic trace.
    Collections
    • CAS: Mathematics & Statistics: Scholarly Papers [263]
    • BU Open Access Articles [3664]


    Boston University
    Contact Us | Send Feedback | Help
     

     

    Browse

    All of OpenBUCommunities & CollectionsIssue DateAuthorsTitlesSubjectsThis CollectionIssue DateAuthorsTitlesSubjects

    Deposit Materials

    LoginNon-BU Registration

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Boston University
    Contact Us | Send Feedback | Help