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Asymptotic properties of random extremes under general normalization from nonidentical distributions

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  • H.M. Barakat

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  • E.M. Nigm
  • M.E. El-Adll
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    Abstract

    In this paper we study the weak convergence of the generally normalized extremes (extremes under nonlinear monotone normalization) of random number of independent (nonidentically distributed) random variables. When the random sample size is assumed to converge in probability and the interrelation between the basic variables and their random size is not restricted, the limit forms as well as the sufficient conditions of convergence are derived. Moreover, when the random sample size is assumed to converge weakly and independent of the basic variables, the necessary and sufficient conditions for the convergence are derived. Copyright Springer-Verlag 2004

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    File URL: http://hdl.handle.net/10.1007/s001840300284
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    Bibliographic Info

    Article provided by Springer in its journal Metrika.

    Volume (Year): 59 (2004)
    Issue (Month): 3 (06)
    Pages: 275-287

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    Handle: RePEc:spr:metrik:v:59:y:2004:i:3:p:275-287

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    Related research

    Keywords: Weak convergence; extremes; general nonlinear normalization; random sample size;

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    Cited by:
    1. AL-Hussaini, Essam K. & El-Adll, Magdy E., 2004. "Asymptotic distribution of normalized maximum under finite mixture models," Statistics & Probability Letters, Elsevier, vol. 70(1), pages 109-117, October.
    2. E. Nigm, 2006. "Bootstrapping extremes of random variables under power normalization," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 15(1), pages 257-269, June.

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