Fitting a Mejzler distribution to extreme value data
type: Item
instance of: scholarly article
title: Fitting a Mejzler distribution to extreme value data
zbMATH Open document ID: 0884.60043
DOI: 10.1016/S0378-3758(96)00137-1
author: Ishay Weissman
published in: Journal of Statistical Planning and Inference
publication date: 1998-01-14T00:00:00Z
review text: In his fundamental work on limit behaviour of normalized maxima from independent (not necessary identically distributed) random variables, D. Mejzler (1956) showed that the limit df \(G\) possesses a \(\log\)-concave property provided that the uniform negligibility condition is met. Denote the class of all such df's by \(\mathcal M\). The author demonstrates a method to fit a given df \(G\in\mathcal M\) to extreme value data, based on his former results (1975) on extremal processes generated by Poisson point processes whose mean measure at \(B=(x,\infty)\) is \(\lambda(x)=-\log G(x)\).
Mathematics Subject Classification ID: 60G70
Mathematics Subject Classification ID: 62E20
zbMATH DE Number: 1038344
zbMATH Keywords: asymptotic distributions
zbMATH Keywords: estimation methods
reviewed by: Elisaveta Pancheva
MaRDI profile type: Publication
cites work: Records in the presence of a linear trend
cites work: Q4040329
cites work: Multivariate extremal processes generated by independent non-identically distributed random variables
description: scientific article; zbMATH DE number 1038344