Year
2009
Paper Type
Master's Thesis
College
College of Arts and Sciences
Degree Name
Master of Science in Mathematical Sciences (MS)
Department
Mathematics & Statistics
First Advisor
Dr. Ping Sa
Second Advisor
Dr. Pali Sen
Rights Statement
http://rightsstatements.org/vocab/InC/1.0/
Third Advisor
Dr. James Gleaton
Abstract
Many distributions of multivariate data in the real world follow a non-normal model with distributions being skewed and/or heavy tailed. In studies in which multivariate non-normal distributions are needed, it is important for simulations of those variables to provide data that is close to the desired parameters while also being fast and easy to perform. Three algorithms for generating multivariate non-normal distributions are reviewed for accuracy, speed and simplicity. They are the Fleishman Power Method, the Fifth-Order Polynomial Transformation Method, and the Generalized Lambda Distribution Method. Simulations were run in order to compare the three methods by how well they generate bivariate distributions with the desired means, variances, skewness, kurtoses, and correlation, simplicity of the algorithms, and how quickly the desired distributions were calculated.
Suggested Citation
Stewart, Jaimee E., "A Comparison of Methods for Generating Bivariate Non-normally Distributed Random Variables" (2009). UNF Graduate Theses and Dissertations. 235.
https://digitalcommons.unf.edu/etd/235