Asymptotic properties of random extremes under general normalization from nonidentical distributions

H.M. Barakat (), E.M. Nigm and M.E. El-Adll

Metrika: International Journal for Theoretical and Applied Statistics, 2004, vol. 59, issue 3, 275-287

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

Keywords: Weak convergence; extremes; general nonlinear normalization; random sample size (search for similar items in EconPapers)
Date: 2004
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Persistent link: https://EconPapers.repec.org/RePEc:spr:metrik:v:59:y:2004:i:3:p:275-287

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DOI: 10.1007/s001840300284

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