Abstract
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.
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.
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 213 - 217 |
| Fachzeitschrift | Computational Statistics and Data Analysis |
| Jahrgang | 55 |
| Ausgabenummer | 1 |
| Publikationsstatus | Veröffentlicht - 1 Feb. 2011 |
Österreichische Systematik der Wissenschaftszweige (ÖFOS)
- 101014 Numerische Mathematik