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

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

Simulating Univariate and Multivariate Nonnormal Distributions through the Method of Percentiles.

This article derives a standard normal-based power method polynomial transformation for Monte Carlo simulation studies, approximating distributions, and fitting distributions to data based on the method of percentiles. The proposed method is used primarily when (1) conventional (or L) moment-based estimators such as skew (or L-skew) and kurtosis (or L -kurtosis) are unknown or (2) data are unav...

Comparing Mean Vectors Via Generalized Inference in Multivariate Log-Normal Distributions

Abstract In this paper, we consider the problem of means in several multivariate log-normal distributions and propose a useful method called as generalized variable method. Simulation studies show that suggested method has a appropriate size and power regardless sample size. To evaluation this method, we compare this method with traditional MANOVA such that the actual sizes of the two methods ...

Simulating Univariate and Multivariate Tukey g-and-h Distributions Based on the Method of Percentiles

A Simple Transformation Method in Skewness Reduction

Statistical analysis of non-normal data is usually more complicated than that for normaldistribution. In this paper, a simple root/power transformation technique developed by Niaki, et al [1]is extended to transform right and left skewed distributions to nearly normal. The value of theroot/power is explored such that the skewness of the transformed data becomes almost zero with anacceptable err...