The Variance-Gamma Distribution: A Review
By A. Fischer, Robert E. Gaunt, Andrey Sarantsev. 2023
37 citations recorded by the source corpus.
The variance-gamma (VG) distributions form a four-parameter family which includes as special and limiting cases the normal, gamma and Laplace distributions. Some of the numerous applications include financial modelling and distributional approximation on Wiener space. In this review, we provide an up-to-date account of the basic distributional theory of the VG distribution. Properties covered include probability and cumulative distribution functions, generating functions, moments and cumulants, mode and median, Stein characterisations, representations in terms of other random variables, and a list of related distributions. We also review methods for parameter estimation and some applications of the VG distribution, including the aforementioned applications to financial modelling and distributional approximation on Wiener space.
Subfields: Computer Vision and Pattern Recognition, Finance, Statistics and Probability, Probabilistic & Bayesian Methods
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Nicolas Papernot
- Zakhar Shumaylov
- Yiren Zhao
- Yarin Gal
- Ross Anderson
- Ilia Shumailov
- Luc Van Gool
- Alan L. Yuille
In Finance
In Statistics and Probability
In Probabilistic & Bayesian Methods
- Nicolas Papernot
- Zakhar Shumaylov
- Yiren Zhao
- Yarin Gal
- Ross Anderson
- Ilia Shumailov
- Zoubin Ghahramani
- S. Murray
Citations in the corpus
- The Curse of Recursion: Training on Generated Data Makes Models Forget (2023, background): “From Equation (3), we see that even after the first approximation, the distribution of X ij is no longer normal, it follows a variance-gamma distribution [Fischer et al., 2023].”