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Simulating Multivariate Extreme Value Distributions of Logistic Type

Simulating Multivariate Extreme Value Distributions of Logistic Type Methods are given for simulating from symmetric and asymmetric versions of the multivariate logistic distribution, and from other multivariate extreme value distributions based on the well known logistic model. We consider two general approaches. The first approach uses transformations to derive random variables with a joint distribution function from which it is easy to simulate. The second approach derives from a specification of conditionally independent marginal components, conditioning on positive stable random variables. This specification extends to models of nested or hierarchical type and leads to an efficient way of incorporating marginal censoring. The algorithms presented in Sections 2 and 3 are available on request from the author. They are also included in the R (Ihaka and Gentleman, 1996) package evd (Stephenson, 2002), which is available from http://www.maths.lancs.ac.uk/~stephena/. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Extremes Springer Journals

Simulating Multivariate Extreme Value Distributions of Logistic Type

Extremes , Volume 6 (1) – Oct 6, 2004

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References (26)

Publisher
Springer Journals
Copyright
Copyright © 2003 by Kluwer Academic Publishers
Subject
Statistics; Hydrogeology; Statistics, general; Statistics for Business/Economics/Mathematical Finance/Insurance; Quality Control, Reliability, Safety and Risk; Civil Engineering; Environmental Management
ISSN
1386-1999
eISSN
1572-915X
DOI
10.1023/A:1026277229992
Publisher site
See Article on Publisher Site

Abstract

Methods are given for simulating from symmetric and asymmetric versions of the multivariate logistic distribution, and from other multivariate extreme value distributions based on the well known logistic model. We consider two general approaches. The first approach uses transformations to derive random variables with a joint distribution function from which it is easy to simulate. The second approach derives from a specification of conditionally independent marginal components, conditioning on positive stable random variables. This specification extends to models of nested or hierarchical type and leads to an efficient way of incorporating marginal censoring. The algorithms presented in Sections 2 and 3 are available on request from the author. They are also included in the R (Ihaka and Gentleman, 1996) package evd (Stephenson, 2002), which is available from http://www.maths.lancs.ac.uk/~stephena/.

Journal

ExtremesSpringer Journals

Published: Oct 6, 2004

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