An approximate method for generating asymmetric random variables

Authors:
John S. Ramberg;Bruce W. Schmeiser
Affiliations:
Univ. of Iowa, Iowa City;Georgia Institute of Technology, Atlanta
Venue:
Communications of the ACM
Year:
1974

Citing 2
Cited 23

An approximate method for generating symmetric random variables

Communications of the ACM
Handbook of Mathematical Functions, With Formulas, Graphs, and Mathematical Tables,

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

Random variate generation in one line of code

WSC '96 Proceedings of the 28th conference on Winter simulation
Advanced input modeling for simulation experimentation

Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
Alternative approaches for specifying input distributions and processes (panel session)

WSC' 90 Proceedings of the 22nd conference on Winter simulation
Numerical maximum likelihood estimation for the g-and-k and generalized g-and-h distributions

Statistics and Computing
Weighted quantile-based estimation for a class of transformation distributions

Computational Statistics & Data Analysis
Fast fifth-order polynomial transforms for generating univariate and multivariate nonnormal distributions

Computational Statistics & Data Analysis
Fitting a distribution to data using an alternative to moments

WSC '79 Proceedings of the 11th conference on Winter simulation - Volume 2
Methods for modelling and generating probabilistic components in digital computer simulation when the standard distributions are not adequate: A survey

WSC '77 Proceedings of the 9th conference on Winter simulation - Volume 1
The generation of order statistics in digital computer simulation: A survey

WSC '78 Proceedings of the 10th conference on Winter simulation - Volume 1
Efficient approximation of response time densities and quantiles in stochastic models

WOSP '04 Proceedings of the 4th international workshop on Software and performance
Methods for modelling and generating probabilistic components in digital computer simulation when the standard distributions are not adequate: a survey

ACM SIGSIM Simulation Digest
Edgeworth-Expanded Gaussian Mixture Density Modeling

Neural Computation
Estimating the parameters of a generalized lambda distribution

Computational Statistics & Data Analysis
A bootstrap approach to test the conditional symmetry in time series models

Computational Statistics & Data Analysis
Numerical maximum log likelihood estimation for generalized lambda distributions

Computational Statistics & Data Analysis
The Robustness of the RESET Test to Non-Normal Error Terms

Computational Economics
Characterizing the generalized lambda distribution by L-moments

Computational Statistics & Data Analysis
On the choice of the smoothing parameter for the BHEP goodness-of-fit test

Computational Statistics & Data Analysis
Bayesian estimation of quantile distributions

Statistics and Computing
Confidence intervals for quantiles using generalized lambda distributions

Computational Statistics & Data Analysis
Cressie and Read power-divergences as influence measures for logistic regression models

Computational Statistics & Data Analysis
Introduction to simulation input modeling

Proceedings of the Winter Simulation Conference
Information estimators for weighted observations

Neural Networks

Quantified Score

Hi-index	48.22

Visualization

Abstract

Tukey's lambda distribution is generalized to provide an algorithm for generating values of unimodal asymmetric random variables. This algorithm has the same advantages as the symmetric random variable generator previously given by the authors, except that the addition of another parameter complicates the problem of finding the parameter values to fit a distribution.