A general approach to generate random variates for multivariate copulae

N. Tajvidi, B. A. Turlach

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We suggest two methods for simulating from a multivariate copula in an arbitrary dimension. Although our main emphasis in this paper is on multivariate extreme value distributions, the proposed methods can be applied to any copula. The basic idea is to approximate the (unknown) density of the copula by a distribution that has a piece-wise constant (histogram) density. This is achieved by partitioning the support of a given copula C into a large number of hyper-rectangles and using them to generate random variates from an approximation of the copula. We suggest two methods for finding this approximation which correspond to either finding hyper-rectangles which have equal probability mass with respect to C, or determining a partition using hyper-squares of equal volume and finding the corresponding probability mass of each hyper-square. We also discuss how the generated random variates can be used as proposals in a Metropolis–Hastings algorithm, when C is an absolutely continuous distribution function, to generate a sequence of random variates from C. An implementation of the proposed methodologies is provided for the statistical computing and graphics environment R in our package called SimCop.

Original languageEnglish
Pages (from-to)140-155
Number of pages16
JournalAustralian and New Zealand Journal of Statistics
Volume60
Issue number1
DOIs
Publication statusPublished - 1 Mar 2018

Fingerprint

Copula
Rectangle
Multivariate Extreme Value Distribution
Statistical Graphics
Statistical Computing
Metropolis-Hastings Algorithm
Continuous Distributions
Approximation
Absolutely Continuous
Histogram
Partitioning
Continuous Function
Distribution Function
Partition
Unknown
Methodology
Arbitrary

Cite this

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A general approach to generate random variates for multivariate copulae. / Tajvidi, N.; Turlach, B. A.

In: Australian and New Zealand Journal of Statistics, Vol. 60, No. 1, 01.03.2018, p. 140-155.

Research output: Contribution to journalArticle

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