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.
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The authors gratefully acknowledge the useful suggestions of the area editor and two anonymous referees that helped to improve the presentation of the paper.
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Appendix
Appendix
1.1 Proof of Lemma 11
Let λ∈[0,1) be fixed. Recall that . Notice that does not contain any point left of the line u=−mv since we have used μ=m in (3), i.e., we have shifted quasi-density g by the mode m to the left, see Fig. 5. Consequently, −mv +≤u − and . Let x + defined as in Sect. 2, i.e., it is the unique root of (5) greater than m. Thus \((x-m)\sqrt{g(x)}\) is monotonically increasing in [m,x +].
Now choose x 1∈(0,x +−m) and let (u 1,v 1) be the point on the boundary of corresponding to x 1, i.e., \(v_{1}=\sqrt{g(x_{1}+m)}\) and u 1=xv 1. Then does not intersect the open rectangle (u 1,u +)×(v 1,v +) and thus
We therefore find
Now let
It is the unique maximum of \(x\sqrt{g(x)}\), see Sect. 5. Since \(((x-m)\sqrt{g(x)} )'\geq (x\sqrt{g(x)} )'\) for all x≥m, we find \(x_{0}^{+}\leq x^{+}\). Now define
Clearly \(u^{*}\leq u^{+}=\sup (x-m)\sqrt{g(x)}\), and thus ε=u +−u ∗≥0. From (10) we then obtain
Now set
We first have to check whether the condition x 1∈(0,x +−m) is fulfilled. For the limits β→0 we find
An immediate consequence is that for sufficiently small β>0, \(x_{1}(\beta) < x_{0}^{+}(\beta)-m(\beta) \leq x^{+}(\beta)-m(\beta)\) which shows that x 1(β)∈(0,x +−m) when β is close enough to zero. Thus inequality (11) holds. Moreover,
For the denominators in (11) we find
Finally,
Collecting all limits we find that all fractions on the right hand side of inequality (11) converge to 0 and thus as claimed.
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Hörmann, W., Leydold, J. Generating generalized inverse Gaussian random variates. Stat Comput 24, 547–557 (2014). https://doi.org/10.1007/s11222-013-9387-3
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DOI: https://doi.org/10.1007/s11222-013-9387-3