Simulation of right and left truncated gamma distributions by mixtures

Authors:
Anne Philippe
Affiliations:
CNRS UPRES-A 60 85, Universite´ de Rouen 76821 Mont Saint Aignan Cedex, France
Venue:
Statistics and Computing
Year:
1997

Citing 2
Cited 5

Reparameterization strategies for hidden Markov models and Bayesian approaches to maximum likelihood estimation

Statistics and Computing
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

Robust Bayesian analysis of heavy-tailed stochastic volatility models using scale mixtures of normal distributions

Computational Statistics & Data Analysis
Estimation in nonlinear mixed-effects models using heavy-tailed distributions

Statistics and Computing
Improved cross-entropy method for estimation

Statistics and Computing
A Bayesian approach to term structure modeling using heavy-tailed distributions

Applied Stochastic Models in Business and Industry
Markov chain importance sampling with applications to rare event probability estimation

Statistics and Computing

Quantified Score

Hi-index	0.01

Visualization

Abstract

We study the properties of truncated gamma distributions and we derive simulation algorithms which dominate the standard algorithms for these distributions. For the right truncated gamma distribution, an optimal accept–reject algorithm is based on the fact that its density can be expressed as an infinite mixture of beta distribution. For integer values of the parameters, the density of the left truncated distributions can be rewritten as a mixture which can be easily generated. We give an optimal accept–reject algorithm for the other values of the parameter. We compare the efficiency of our algorithm with the previous method and show the improvement in terms of minimum acceptance probability. The algorithm proposed here has an acceptance probability which is superior to e/4.