Generating Generalized Inverse Gaussian Random Variates

Hörmann, Wolfgang and Leydold, Josef (2013) Generating Generalized Inverse Gaussian Random Variates. Research Report Series / Department of Statistics and Mathematics, 123. WU Vienna University of Economics and Business, Vienna.


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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)

Item Type: Paper
Additional Information: This paper has been accepted for publication in Statistics and Computing.
Keywords: random variate generation / generalized inverse Gaussian distribution / varying parameters / Zufallsvariable / Zufallszahl / Stochastische Erzeugung / Zufallsgenerator / Inverse Normalverteilung
Classification Codes: RVK QH 230, SK 820, SK 800, SK 835
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Depositing User: Josef Leydold
Date Deposited: 19 Feb 2013 08:25
Last Modified: 22 Oct 2019 00:41


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