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313814

Weak Convergence of Random Extremes from Non-identical Distributions Under General Normalization

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Last updated: 28 Dec 2024

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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 (non-identically) random variables. When the random sample size is assumed to be converged 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 be converged weakly and independent of the basic variables, the necessary and sufficient conditions for the convergence are derived.  

DOI

10.21608/esju.2001.313814

Keywords

Weak Convergence, Extremes - General Nonlinear Normalization - Random Sample Size

Volume

45

Article Issue

2

Related Issue

43029

Issue Date

2001-12-01

Publish Date

2001-12-01

Page Start

182

Page End

198

Print ISSN

0542-1748

Online ISSN

2786-0086

Link

https://esju.journals.ekb.eg/article_313814.html

Detail API

https://esju.journals.ekb.eg/service?article_code=313814

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6

Type

Original Article

Type Code

1,914

Publication Type

Journal

Publication Title

The Egyptian Statistical Journal

Publication Link

https://esju.journals.ekb.eg/

MainTitle

Weak Convergence of Random Extremes from Non-identical Distributions Under General Normalization

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Article

Created At

28 Dec 2024