Generating generalized inverse Gaussian random variates View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2014-07

AUTHORS

Wolfgang Hörmann, Josef Leydold

ABSTRACT

The generalized inverse Gaussian distribution has become quite popular in financial engineering. The most popular random variate generator is due to Dagpunar (Commun. Stat., Simul. Comput. 18:703–710, 1989). It is an acceptance-rejection algorithm method based on the Ratio-of-Uniforms method. However, it is not uniformly fast as it has a prohibitive large rejection constant when the distribution is close to the gamma distribution. Recently some papers have discussed universal methods that are suitable for this distribution. However, these methods require an expensive setup and are therefore not suitable for the varying parameter case which occurs in, e.g., Gibbs sampling. In this paper we analyze the performance of Dagpunar’s algorithm and combine it with a new rejection method which ensures a uniformly fast generator. As its setup is rather short it is in particular suitable for the varying parameter case. More... »

PAGES

547-557

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11222-013-9387-3

DOI

http://dx.doi.org/10.1007/s11222-013-9387-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1030247991


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