Quotation Hörmann, Wolfgang, Leydold, Josef. 2013. Generating Generalized Inverse Gaussian Random Variates, DOI 10.1007/s11222-013-9387-3. Statistics and Computing 1-11.


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Abstract

The generalized inverse Gaussian distribution has become quite popular in financial engineering. The most popular random variate generator is due to Dagpunar (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. (authors' abstract)

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Statistics and Computing
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D
Language English
Title Generating Generalized Inverse Gaussian Random Variates, DOI 10.1007/s11222-013-9387-3
Year 2013
Page from 1
Page to 11
URL http://link.springer.com/article/10.1007%2Fs11222-013-9387-3

Associations

People
Leydold, Josef (Details)
External
Hörmann, Wolfgang
Organization
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
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