On Polynomial Transformations For Simulating Multivariate Non-normal Distributions

On Polynomial Transformations For Simulating Multivariate Non-normal Distributions

Procedures are introduced and discussed for increasing the computational and statistical efficiency of polynomial transformations used in Monte Carlo or simulation studies. Comparisons are also made between polynomials of order three and five in terms of (a) computational and statistical efficiency, (b) the skew and kurtosis boundary, and (c) boundaries for Pearson correlations. It is also show...

On simulating multivariate non-normal distributions from the generalized lambda distribution

The class of generalized lambda distributions (GLDs) is primarily used for modeling univariate real-world data. The GLD has not been as popular as some other methods for simulating observations from multivariate distributions because of computational difficulties. In view of this, the methodology and algorithms are presented for extending the GLD from univariate to multivariate data generation ...

A Method for Simulating Multivariate Non Normal Distributions with Specified Standardized Cumulants and Intraclass Correlation Coefficients

A Percentile-Based Power Method in SAS: Simulating Multivariate Non-normal Continuous Distributions

The conventional power method transformation is a moment-matching technique that simulates non-normal distributions with controlled measures of skew and kurtosis. The percentile-based power method is an alternative that uses the percentiles of a distribution in lieu of moments. This article presents a SAS/IML macro that implements the percentile-based power method.

JMASM35: A Percentile-Based Power Method: Simulating Multivariate Non-normal Continuous Distributions (SAS)