Generating generalized inverse Gaussian random variates by fast inversion

被引：6

作者：

Leydold, Josef ^{[1
]}

Hormann, Wolfgang ^{[2
]}

机构：

[1] WU Vienna Univ Econ & Business, Dept Math & Stat, A-1090 Vienna, Austria

[2] Bogazici Univ, Dept Ind Engn, TR-34342 Bebek, Turkey

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2011年 / 55卷 / 01期

关键词：

Generalized inverse Gaussian distribution; Random variate generation; Numerical inversion;

D O I：

10.1016/j.csda.2010.07.011

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The inversion method for generating non-uniformly distributed random variates is a crucial part in many applications of Monte Carlo techniques, e.g., when low discrepancy sequences or copula based models are used. Unfortunately, closed form expressions of quantile functions of important distributions are often not available. The (generalized) inverse Gaussian distribution is a prominent example. It is shown that algorithms that are based on polynomial approximation are well suited for this distribution. Their precision is close to machine precision and they are much faster than root finding methods like the bisection method that has been recently proposed. (C) 2010 Elsevier By. All rights reserved.

引用

页码：213 / 217

页数：5

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