Quotation Leydold, Josef, Hörmann, Wolfgang. 2011. Generating generalized inverse Gaussian random variates by fast inversion. Computational Statistics & Data Analysis 55 (1): 213-217.




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.


Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Computational Statistics and Data Analysis
Citation Index SCI
WU-Journal-Rating new FIN-A, VW-D, WH-B
Language English
Title Generating generalized inverse Gaussian random variates by fast inversion
Volume 55
Number 1
Year 2011
Page from 213
Page to 217
Reviewed? Y


Leydold, Josef (Details)
Hörmann, Wolfgang (Turkey)
Institute for Statistics and Mathematics IN (Details)
Research Institute for Computational Methods FI (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1114 Numerical mathematics (Details)
Google Scholar: Search